Steps

Database consolidation

We selected women at baseline (21,423) in the following variables of interest:

  • ‘Type of program’(tipo_de_programa_2)
  • ‘Marital status’(estado_conyugal_2)
  • ‘Educational Attainment’ (escolaridad_rec)= We selected the most vulnerable category among continous treatments for the same user.
  • ‘Age at admission to treatment, grouped’(edad_al_ing_grupos)= We grouped people with less than 18 years old (0.21%) with ‘18-29’ years old (36.80%).
  • ‘Consumption frequency of primary or main substance’(freq_cons_sus_prin)= Among the categories of the Frequency of Drug Consumption, we grouped (6%) with Did not use in the last 30 days (2%)
  • ‘Pregnant at admission’(embarazo)
  • ‘Tenure status of households’ (tenencia_de_la_vivienda_mod)= Three categories were collapsed into one single condition: Owner (29%), with Transferred dwellings (4%) and Pays Dividend (2%).
  • ‘Primary or main substance’(sus_principal_mod)= We used the primary substance at admission of the largest treatment for continuous treatments. Every substance was chosen from the largest treatment, excepting cases where the value was not available in the largest treatment.
  • ‘Co-occurring SUD’ (num_otras_sus_mod)= We counted the times that different substances at admission appear for each entry.
  • ‘Number of children (max. Value) (Dichotomized)’(numero_de_hijos_mod_rec)= We selected the number of children with the maximum value among continuous treatments. We decided to dichotomize between no children (12%) and children (12%).
## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 21423 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
## Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be
## incorrect
## Warning in compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
## Some levels of 'tipo_de_plan_res' are removed since no observation in that/those
## levels
Table 1. Summary descriptives in Women, Between the Different Programs
Variables General population Women Specific Sig.
N=13196 N=8210
Marital status: <0.001
Married/Shared living arrangements 4628 (35.1%) 2325 (28.3%)
Separated/Divorced 1737 (13.2%) 869 (10.6%)
Single 6431 (48.7%) 4860 (59.2%)
Widower 372 (2.82%) 150 (1.83%)
‘Missing’ 28 (0.21%) 6 (0.07%)
Age at admission to treatment, grouped.: .
<18-29 4545 (34.4%) 3379 (41.2%)
30-39 4273 (32.4%) 2750 (33.5%)
40-49 2650 (20.1%) 1380 (16.8%)
50+ 1725 (13.1%) 700 (8.53%)
‘Missing’ 3 (0.02%) 1 (0.01%)
Educational Attainment: <0.001
3-Completed primary school or less 4296 (32.6%) 2645 (32.2%)
2-Completed high school or less 6644 (50.3%) 4285 (52.2%)
1-More than high school 2161 (16.4%) 1260 (15.3%)
‘Missing’ 95 (0.72%) 20 (0.24%)
Primary or main substance: .
Alcohol 4846 (36.7%) 1905 (23.2%)
Cocaine hydrochloride 2529 (19.2%) 1441 (17.6%)
Marijuana 953 (7.22%) 475 (5.79%)
Other 563 (4.27%) 273 (3.33%)
Cocaine paste 4305 (32.6%) 4115 (50.1%)
‘Missing’ 0 (0.00%) 1 (0.01%)
Consumption frequency of primary or main substance: <0.001
Less than 1 day a week 844 (6.40%) 217 (2.64%)
2 to 3 days a week 3896 (29.5%) 1682 (20.5%)
4 to 6 days a week 2055 (15.6%) 1177 (14.3%)
1 day a week or more 1046 (7.93%) 295 (3.59%)
Daily 5262 (39.9%) 4816 (58.7%)
‘Missing’ 93 (0.70%) 23 (0.28%)
Biopsychosocial involvement: 0.000
1-Mild 1302 (9.87%) 161 (1.96%)
2-Moderate 7740 (58.7%) 3335 (40.6%)
3-Severe 3876 (29.4%) 4621 (56.3%)
‘Missing’ 278 (2.11%) 93 (1.13%)
Tenure status of households: <0.001
Illegal Settlement 180 (1.36%) 146 (1.78%)
Others 343 (2.60%) 223 (2.72%)
Owner/Transferred dwellings/Pays Dividends 4851 (36.8%) 2474 (30.1%)
Renting 2667 (20.2%) 1352 (16.5%)
Stays temporarily with a relative 4561 (34.6%) 3669 (44.7%)
‘Missing’ 594 (4.50%) 346 (4.21%)
Co-occurring SUD: .
No additional substance 4398 (33.3%) 1732 (21.1%)
One additional substance 4903 (37.2%) 3214 (39.1%)
More than one additional substance 3895 (29.5%) 3263 (39.7%)
‘Missing’ 0 (0.00%) 1 (0.01%)
Have children (Dichotomized): 0.003
No 1672 (12.7%) 921 (11.2%)
Yes 11468 (86.9%) 7263 (88.5%)
‘Missing’ 56 (0.42%) 26 (0.32%)
Setting of Treatment: 0.000
Outpatient 12581 (95.3%) 4880 (59.4%)
Residential 615 (4.66%) 3330 (40.6%)
Note. Variables of C1 dataset had to be standardized before comparison;
Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;
Categorical variables are presented as number (%)


Imputation


We generated a plot to see all the missing values in the sample.


#<div style="border: 1px solid #ddd; padding: 5px; overflow-y: scroll; height:400px; overflow-x: scroll; width:100%">
library(dplyr)
library(ggplot2)

vector_variables<-
c("tipo_de_programa_2", "estado_conyugal_2", "edad_al_ing_grupos", "escolaridad_rec", "sus_principal_mod", "freq_cons_sus_prin", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod", "num_otras_sus_mod", "numero_de_hijos_mod_rec", "motivodeegreso_mod_imp")

missing.values<-
CONS_C1_df_dup_SEP_2020_women %>%
  rowwise %>%
  dplyr::mutate_at(.vars = vars(vector_variables),
                   .funs = ~ifelse(is.na(.), 1, 0)) %>% 
  dplyr::ungroup() %>% 
  dplyr::summarise_at(vars(vector_variables),~sum(.))
#t(missing.values)

miss_val_bar<-
melt(missing.values) %>% 
    mutate(perc=scales::percent(value/nrow(CONS_C1_df_dup_SEP_2020_women))) %>% 
    arrange(desc(perc))

plot_miss<-
missing.values %>%
  data.table::melt() %>%  #condicion_ocupacional_corr
  dplyr::filter(!variable %in% c("row", "hash_key", "dias_treat_imp_sin_na", "dup")) %>% 
  dplyr::mutate(perc= value/sum(nrow(CONS_C1_df_dup_SEP_2020_women))) %>% 
  dplyr::mutate(label_text= paste0("Variable= ",variable,"<br>n= ",value,"<br>",scales::percent(round(perc,3)))) %>%
  dplyr::mutate(perc=perc*100) %>% 
  ggplot() +
  geom_bar(aes(x=factor(variable), y=perc,label= label_text), stat = 'identity') +
  sjPlot::theme_sjplot()+
#  scale_y_continuous(limits=c(0,1), labels=percent)+
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size=9))+
  labs(x=NULL, y="% of Missing Values", caption=paste0("Nota. Percentage of missing values (n= ",sum(nrow(CONS_C1_df_dup_SEP_2020_women)),")"))

  ggplotly(plot_miss, tooltip = c("label_text"))%>% layout(xaxis= list(showticklabels = T), height = 600, width=800) %>%   layout(yaxis = list(tickformat='%',  range = c(0, 5)))

Figure 3. Bar plot of Percentage of Missing Values per Variables at Basline

  #</div>






From the figure above, we could see that the Tenure status of households (tenencia_de_la_vivienda_mod) and the Biopsychosocial Involvement (ompromiso_biopsicosocial) had a proportion of missing values, but no greater than 5%. This is why we imputed these values under MAR assumption.


vector_variables_only_for_imputation<-
c("row", "hash_key", "tipo_de_programa_2", "estado_conyugal_2", "edad_al_ing_grupos", "escolaridad_rec", "sus_principal_mod", "freq_cons_sus_prin", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod", "num_otras_sus_mod", "numero_de_hijos_mod_rec","motivodeegreso_mod_imp")

#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:

  #HACER BASE ESPECIAL QUE CONTENGA UNA VARIABLE DE EDAD DE INICIO DE CONSUMO DE SUSTANCIA PRINCIPAL PARA EQUIPARAR
CONS_C1_df_dup_SEP_2020_women_miss<-
CONS_C1_df_dup_SEP_2020_women %>% 
    #dplyr::group_by(hash_key) %>% 
    #dplyr::mutate(rn=row_number()) %>% 
    #dplyr::ungroup() %>% 
  
  #:#:#:#:#:#:#:#:#:#:#:
  # ORDINALIZAR LAS VARIABLES ORDINALES: 
  dplyr::select_(.dots = vector_variables_only_for_imputation) %>% 
    data.table::data.table()
  
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(evaluacindelprocesoteraputico) 
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:

library(Amelia)

amelia_fit <- amelia(CONS_C1_df_dup_SEP_2020_women_miss, 
#Warning message:
#In amcheck(x = x, m = m, idvars = numopts$idvars, priors = priors,  : 
#The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.
                     m=61, 
                     parallel = "multicore",
                     idvars="row",
                     noms= c("tipo_de_programa_2", "estado_conyugal_2", "sus_principal_mod", "tenencia_de_la_vivienda_mod", "numero_de_hijos_mod_rec","motivodeegreso_mod_imp"),
                     ords= c("edad_al_ing_grupos", "escolaridad_rec", "freq_cons_sus_prin", "compromiso_biopsicosocial", "num_otras_sus_mod"),
                     cs = "hash_key",
                     incheck = TRUE)
# Se sacó el servicio de salud porque tiene mucha información: The number of categories in one of the variables marked nominal has greater than 10 categories. Check nominal specification.

#Error in yy %*% unique(na.omit(x.orig[, i])) :  non-conformable arguments.


Age at Admission to Treatment (in groups)

We started looking over the missing values in the age at admission (in groups) (n=4).


#On this graph, a y = x line indicates the line of perfect agreement; that is, if the imputation model was a perfect predictor of the true value, all the imputations would fall on this line
no_mostrar=0
if(no_mostrar==1){
  res <- { 
    setTimeLimit(nn_K*500)
    ovr_imp_edad_ini_cons<-overimpute(amelia_fit, var = "edad_al_ing_grupos")
  }
}

paste0("Users that had more than one treatment with no date of admission: ",CONS_C1_df_dup_SEP_2020_women_miss %>% 
    dplyr::group_by(hash_key) %>% 
    dplyr::mutate(na_edad_ing=sum(is.na(edad_al_ing_grupos))) %>% 
    dplyr::ungroup() %>% 
    dplyr::filter(na_edad_ing>0) %>% 
    dplyr::group_by(hash_key) %>% 
    dplyr::summarise(n=n()) %>% dplyr::filter(n>1) %>% nrow())
## [1] "Users that had more than one treatment with no date of admission: 0"
#Hay poca relación en las imputaciones.
#table(is.na(CONS_C1_df_dup_SEP_2020_women_not_miss$edad_al_ing),exclude=NULL)

edad_al_ing_grupos_imputed<-
  cbind.data.frame(amelia_fit$imputations$imp1$row,
       amelia_fit$imputations$imp1$edad_al_ing_grupos,
       amelia_fit$imputations$imp2$edad_al_ing_grupos,
       amelia_fit$imputations$imp3$edad_al_ing_grupos,
       amelia_fit$imputations$imp4$edad_al_ing_grupos,
       amelia_fit$imputations$imp5$edad_al_ing_grupos,
       amelia_fit$imputations$imp6$edad_al_ing_grupos,
       amelia_fit$imputations$imp7$edad_al_ing_grupos,
       amelia_fit$imputations$imp8$edad_al_ing_grupos,
       amelia_fit$imputations$imp9$edad_al_ing_grupos,
       amelia_fit$imputations$imp10$edad_al_ing_grupos,
       amelia_fit$imputations$imp11$edad_al_ing_grupos,
       amelia_fit$imputations$imp12$edad_al_ing_grupos,
       amelia_fit$imputations$imp13$edad_al_ing_grupos,
       amelia_fit$imputations$imp14$edad_al_ing_grupos,
       amelia_fit$imputations$imp15$edad_al_ing_grupos,
       amelia_fit$imputations$imp16$edad_al_ing_grupos,
       amelia_fit$imputations$imp17$edad_al_ing_grupos,
       amelia_fit$imputations$imp18$edad_al_ing_grupos,
       amelia_fit$imputations$imp19$edad_al_ing_grupos,
       amelia_fit$imputations$imp20$edad_al_ing_grupos,
       amelia_fit$imputations$imp21$edad_al_ing_grupos,
       amelia_fit$imputations$imp22$edad_al_ing_grupos,
       amelia_fit$imputations$imp23$edad_al_ing_grupos,
       amelia_fit$imputations$imp24$edad_al_ing_grupos,
       amelia_fit$imputations$imp25$edad_al_ing_grupos,
       amelia_fit$imputations$imp26$edad_al_ing_grupos,
       amelia_fit$imputations$imp27$edad_al_ing_grupos,
       amelia_fit$imputations$imp28$edad_al_ing_grupos,
       amelia_fit$imputations$imp29$edad_al_ing_grupos,
       amelia_fit$imputations$imp30$edad_al_ing_grupos,
       amelia_fit$imputations$imp31$edad_al_ing_grupos,
       amelia_fit$imputations$imp32$edad_al_ing_grupos,
       amelia_fit$imputations$imp33$edad_al_ing_grupos,
       amelia_fit$imputations$imp34$edad_al_ing_grupos,
       amelia_fit$imputations$imp35$edad_al_ing_grupos,
       amelia_fit$imputations$imp36$edad_al_ing_grupos,
       amelia_fit$imputations$imp37$edad_al_ing_grupos,
       amelia_fit$imputations$imp38$edad_al_ing_grupos,
       amelia_fit$imputations$imp39$edad_al_ing_grupos,
       amelia_fit$imputations$imp40$edad_al_ing_grupos,
       amelia_fit$imputations$imp41$edad_al_ing_grupos,
       amelia_fit$imputations$imp42$edad_al_ing_grupos,
       amelia_fit$imputations$imp43$edad_al_ing_grupos,
       amelia_fit$imputations$imp44$edad_al_ing_grupos,
       amelia_fit$imputations$imp45$edad_al_ing_grupos,
       amelia_fit$imputations$imp46$edad_al_ing_grupos,
       amelia_fit$imputations$imp47$edad_al_ing_grupos,
       amelia_fit$imputations$imp48$edad_al_ing_grupos,
       amelia_fit$imputations$imp49$edad_al_ing_grupos,
       amelia_fit$imputations$imp50$edad_al_ing_grupos,
       amelia_fit$imputations$imp51$edad_al_ing_grupos,
       amelia_fit$imputations$imp52$edad_al_ing_grupos,
       amelia_fit$imputations$imp53$edad_al_ing_grupos,
       amelia_fit$imputations$imp54$edad_al_ing_grupos,
       amelia_fit$imputations$imp55$edad_al_ing_grupos,
       amelia_fit$imputations$imp56$edad_al_ing_grupos,
       amelia_fit$imputations$imp57$edad_al_ing_grupos,
       amelia_fit$imputations$imp58$edad_al_ing_grupos,
       amelia_fit$imputations$imp59$edad_al_ing_grupos,
       amelia_fit$imputations$imp60$edad_al_ing_grupos,
       amelia_fit$imputations$imp61$edad_al_ing_grupos
        ) %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  #18-29 30-39 40-49 50+
  janitor::clean_names() %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::ungroup() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>%
  dplyr::summarise(edad_18_29=sum(value == "<18-29",na.rm=T),
                   edad_30_39=sum(value == "30-39",na.rm=T),
                   edad_40_49=sum(value == "40-49",na.rm=T),
                  edad_50mas=sum(value =="50+",na.rm=T)) %>% 
  dplyr::ungroup() %>% 
  #dplyr::mutate(edad_suma = base::rowSums(dplyr::select(is.na(.),starts_with("edad"))))
  dplyr::mutate(ties= base::rowSums(dplyr::select(.,starts_with("edad"))>0)) %>% 
  dplyr::mutate(edad_al_ing_grupos_imp= dplyr::case_when(
      (edad_18_29> edad_30_39) & (edad_18_29> edad_40_49) & (edad_18_29> edad_50mas)~"<18-29",
      (edad_30_39> edad_18_29) & (edad_30_39> edad_40_49) & (edad_30_39> edad_50mas)~"30-39",
      (edad_40_49> edad_18_29) & (edad_40_49> edad_30_39) & (edad_40_49> edad_50mas)~"40-49",
      (edad_50mas> edad_18_29) & (edad_50mas> edad_30_39) & (edad_50mas> edad_40_49)~"50+"
      )) 

#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:
# Reemplazo los valores perdidos:
CONS_C1_df_dup_SEP_2020_women_miss0<-
CONS_C1_df_dup_SEP_2020_women_miss %>% 
  dplyr::left_join(edad_al_ing_grupos_imputed,by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
  #si la edad al ingreso no existe, el valor promedio imutado es
  dplyr::mutate(edad_al_ing_grupos=dplyr::case_when(is.na(edad_al_ing_grupos)~edad_al_ing_grupos_imp,
                                                    T~as.character(edad_al_ing_grupos))) %>% 
  dplyr::select(-edad_18_29, -edad_30_39, -edad_40_49, -edad_50mas, -ties, -edad_al_ing_grupos_imp)

if(nrow(CONS_C1_df_dup_SEP_2020_women_miss0)-nrow(CONS_C1_df_dup_SEP_2020_women_miss)>0){
  warning("AGS: Some rows were added in the imputation")}


After the imputation, there were no missing cases left.


Primary or main substance

Then we imputed the primary/main substance at admission (n= 1).


# Ver distintos valores propuestos para sustancia de inciio
sus_principal_mod_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
       amelia_fit$imputations$imp1$sus_principal_mod,
       amelia_fit$imputations$imp2$sus_principal_mod,
       amelia_fit$imputations$imp3$sus_principal_mod,
       amelia_fit$imputations$imp4$sus_principal_mod,
       amelia_fit$imputations$imp5$sus_principal_mod,
       amelia_fit$imputations$imp6$sus_principal_mod,
       amelia_fit$imputations$imp7$sus_principal_mod,
       amelia_fit$imputations$imp8$sus_principal_mod,
       amelia_fit$imputations$imp9$sus_principal_mod,
       amelia_fit$imputations$imp10$sus_principal_mod,
       amelia_fit$imputations$imp11$sus_principal_mod,
       amelia_fit$imputations$imp12$sus_principal_mod,
       amelia_fit$imputations$imp13$sus_principal_mod,
       amelia_fit$imputations$imp14$sus_principal_mod,
       amelia_fit$imputations$imp15$sus_principal_mod,
       amelia_fit$imputations$imp16$sus_principal_mod,
       amelia_fit$imputations$imp17$sus_principal_mod,
       amelia_fit$imputations$imp18$sus_principal_mod,
       amelia_fit$imputations$imp19$sus_principal_mod,
       amelia_fit$imputations$imp20$sus_principal_mod,
       amelia_fit$imputations$imp21$sus_principal_mod,
       amelia_fit$imputations$imp22$sus_principal_mod,
       amelia_fit$imputations$imp23$sus_principal_mod,
       amelia_fit$imputations$imp24$sus_principal_mod,
       amelia_fit$imputations$imp25$sus_principal_mod,
       amelia_fit$imputations$imp26$sus_principal_mod,
       amelia_fit$imputations$imp27$sus_principal_mod,
       amelia_fit$imputations$imp28$sus_principal_mod,
       amelia_fit$imputations$imp29$sus_principal_mod,
       amelia_fit$imputations$imp30$sus_principal_mod,
       amelia_fit$imputations$imp31$sus_principal_mod,
       amelia_fit$imputations$imp32$sus_principal_mod,
       amelia_fit$imputations$imp33$sus_principal_mod,
       amelia_fit$imputations$imp34$sus_principal_mod,
       amelia_fit$imputations$imp35$sus_principal_mod,
       amelia_fit$imputations$imp36$sus_principal_mod,
       amelia_fit$imputations$imp37$sus_principal_mod,
       amelia_fit$imputations$imp38$sus_principal_mod,
       amelia_fit$imputations$imp39$sus_principal_mod,
       amelia_fit$imputations$imp40$sus_principal_mod,
       amelia_fit$imputations$imp41$sus_principal_mod,
       amelia_fit$imputations$imp42$sus_principal_mod,
       amelia_fit$imputations$imp43$sus_principal_mod,
       amelia_fit$imputations$imp44$sus_principal_mod,
       amelia_fit$imputations$imp45$sus_principal_mod,
       amelia_fit$imputations$imp46$sus_principal_mod,
       amelia_fit$imputations$imp47$sus_principal_mod,
       amelia_fit$imputations$imp48$sus_principal_mod,
       amelia_fit$imputations$imp49$sus_principal_mod,
       amelia_fit$imputations$imp50$sus_principal_mod,
       amelia_fit$imputations$imp51$sus_principal_mod,
       amelia_fit$imputations$imp52$sus_principal_mod,
       amelia_fit$imputations$imp53$sus_principal_mod,
       amelia_fit$imputations$imp54$sus_principal_mod,
       amelia_fit$imputations$imp55$sus_principal_mod,
       amelia_fit$imputations$imp56$sus_principal_mod,
       amelia_fit$imputations$imp57$sus_principal_mod,
       amelia_fit$imputations$imp58$sus_principal_mod,
       amelia_fit$imputations$imp59$sus_principal_mod,
       amelia_fit$imputations$imp60$sus_principal_mod,
       amelia_fit$imputations$imp61$sus_principal_mod
       )  %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  #18-29 30-39 40-49 50+
  janitor::clean_names() %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::ungroup() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>%
  dplyr::summarise(sus_prin_mar=sum(value == "Marijuana",na.rm=T),
                   sus_prin_oh=sum(value == "Alcohol",na.rm=T),
                   sus_prin_pb=sum(value == "Cocaine paste",na.rm=T),
                  sus_prin_coc=sum(value =="Cocaine hydrochloride",na.rm=T),
                  sus_prin_other=sum(value =="Other",na.rm=T)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(ties= base::rowSums(dplyr::select(.,starts_with("sus_prin_"))>0)) %>% 
  dplyr::mutate(sus_principal_mod_imp= dplyr::case_when(
  (sus_prin_mar> sus_prin_oh)& (sus_prin_mar> sus_prin_pb)& (sus_prin_mar> sus_prin_coc)& (sus_prin_mar> sus_prin_other)~"Marijuana",
  (sus_prin_oh> sus_prin_mar)& (sus_prin_oh> sus_prin_pb)& (sus_prin_oh> sus_prin_coc)& (sus_prin_oh> sus_prin_other)~"Alcohol",
  (sus_prin_pb> sus_prin_mar)& (sus_prin_pb> sus_prin_oh)& (sus_prin_pb> sus_prin_coc)& (sus_prin_pb> sus_prin_other)~"Cocaine paste",
  (sus_prin_coc> sus_prin_mar)& (sus_prin_coc> sus_prin_oh)& (sus_prin_coc> sus_prin_pb)& (sus_prin_coc> sus_prin_other)~"Cocaine hydrochloride",
  (sus_prin_other> sus_prin_mar)& (sus_prin_other> sus_prin_oh)& (sus_prin_other> sus_prin_pb)& (sus_prin_other> sus_prin_coc)~"Cocaine hydrochloride"
  )) 
## `summarise()` ungrouping output (override with `.groups` argument)
#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:
CONS_C1_df_dup_SEP_2020_women_miss1<-
CONS_C1_df_dup_SEP_2020_women_miss0 %>% 
   dplyr::left_join(sus_principal_mod_imputed, by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(sus_principal_mod=factor(dplyr::case_when(is.na(sus_principal_mod)~as.character(sus_principal_mod_imp),
                                 TRUE~as.character(sus_principal_mod)))) %>% 
  dplyr::select(-c(sus_prin_mar, sus_prin_oh, sus_prin_pb, sus_prin_coc, sus_prin_other, ties, sus_principal_mod_imp)) %>% 
  data.table()
#_#_#_#_#_#_#__#_##_#_#_#_#_#_#_#_#_#_#_#_#__#_##_#_#_#_#_##_#_#_#_#_#_#__#_##_#_#_#_#_#_#_#_#_#_#_#_#__#_##_#_#_#_#_#
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss1)-nrow(CONS_C1_df_dup_SEP_2020_women_miss0)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were no missing values.


Co-occurring SUD

Another variable worth imputing is the presence of additional Substance Use Disorders (n= 1).


#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
num_otras_sus_mod_imputed<-
  cbind.data.frame(amelia_fit$imputations$imp1$row,
       amelia_fit$imputations$imp1$num_otras_sus_mod,
       amelia_fit$imputations$imp2$num_otras_sus_mod,
       amelia_fit$imputations$imp3$num_otras_sus_mod,
       amelia_fit$imputations$imp4$num_otras_sus_mod,
       amelia_fit$imputations$imp5$num_otras_sus_mod,
       amelia_fit$imputations$imp6$num_otras_sus_mod,
       amelia_fit$imputations$imp7$num_otras_sus_mod,
       amelia_fit$imputations$imp8$num_otras_sus_mod,
       amelia_fit$imputations$imp9$num_otras_sus_mod,
       amelia_fit$imputations$imp10$num_otras_sus_mod,
       amelia_fit$imputations$imp11$num_otras_sus_mod,
       amelia_fit$imputations$imp12$num_otras_sus_mod,
       amelia_fit$imputations$imp13$num_otras_sus_mod,
       amelia_fit$imputations$imp14$num_otras_sus_mod,
       amelia_fit$imputations$imp15$num_otras_sus_mod,
       amelia_fit$imputations$imp16$num_otras_sus_mod,
       amelia_fit$imputations$imp17$num_otras_sus_mod,
       amelia_fit$imputations$imp18$num_otras_sus_mod,
       amelia_fit$imputations$imp19$num_otras_sus_mod,
       amelia_fit$imputations$imp20$num_otras_sus_mod,
       amelia_fit$imputations$imp21$num_otras_sus_mod,
       amelia_fit$imputations$imp22$num_otras_sus_mod,
       amelia_fit$imputations$imp23$num_otras_sus_mod,
       amelia_fit$imputations$imp24$num_otras_sus_mod,
       amelia_fit$imputations$imp25$num_otras_sus_mod,
       amelia_fit$imputations$imp26$num_otras_sus_mod,
       amelia_fit$imputations$imp27$num_otras_sus_mod,
       amelia_fit$imputations$imp28$num_otras_sus_mod,
       amelia_fit$imputations$imp29$num_otras_sus_mod,
       amelia_fit$imputations$imp30$num_otras_sus_mod,
       amelia_fit$imputations$imp31$num_otras_sus_mod,
       amelia_fit$imputations$imp32$num_otras_sus_mod,
       amelia_fit$imputations$imp33$num_otras_sus_mod,
       amelia_fit$imputations$imp34$num_otras_sus_mod,
       amelia_fit$imputations$imp35$num_otras_sus_mod,
       amelia_fit$imputations$imp36$num_otras_sus_mod,
       amelia_fit$imputations$imp37$num_otras_sus_mod,
       amelia_fit$imputations$imp38$num_otras_sus_mod,
       amelia_fit$imputations$imp39$num_otras_sus_mod,
       amelia_fit$imputations$imp40$num_otras_sus_mod,
       amelia_fit$imputations$imp41$num_otras_sus_mod,
       amelia_fit$imputations$imp42$num_otras_sus_mod,
       amelia_fit$imputations$imp43$num_otras_sus_mod,
       amelia_fit$imputations$imp44$num_otras_sus_mod,
       amelia_fit$imputations$imp45$num_otras_sus_mod,
       amelia_fit$imputations$imp46$num_otras_sus_mod,
       amelia_fit$imputations$imp47$num_otras_sus_mod,
       amelia_fit$imputations$imp48$num_otras_sus_mod,
       amelia_fit$imputations$imp49$num_otras_sus_mod,
       amelia_fit$imputations$imp50$num_otras_sus_mod,
       amelia_fit$imputations$imp51$num_otras_sus_mod,
       amelia_fit$imputations$imp52$num_otras_sus_mod,
       amelia_fit$imputations$imp53$num_otras_sus_mod,
       amelia_fit$imputations$imp54$num_otras_sus_mod,
       amelia_fit$imputations$imp55$num_otras_sus_mod,
       amelia_fit$imputations$imp56$num_otras_sus_mod,
       amelia_fit$imputations$imp57$num_otras_sus_mod,
       amelia_fit$imputations$imp58$num_otras_sus_mod,
       amelia_fit$imputations$imp59$num_otras_sus_mod,
       amelia_fit$imputations$imp60$num_otras_sus_mod,
       amelia_fit$imputations$imp61$num_otras_sus_mod
       ) %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::ungroup() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>%
  dplyr::summarise(no_ad_subs=sum(value == "No additional substance",na.rm=T),
                   one_ad_subs=sum(value == "One additional substance",na.rm=T),
                   more_one_ad_subs=sum(value == "More than one additional substance",na.rm=T)) %>% 
  dplyr::ungroup() %>% 
# Hacer la variable imputada
  dplyr::mutate(num_otras_sus_mod_imp= dplyr::case_when(
      (no_ad_subs>one_ad_subs)&(no_ad_subs>more_one_ad_subs)~"No additional substance",
      (one_ad_subs>no_ad_subs)&(one_ad_subs>more_one_ad_subs)~"One additional substance",
      (more_one_ad_subs>no_ad_subs)&(more_one_ad_subs>one_ad_subs)~"More than one additional substance",
      T~NA_character_))

#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:

CONS_C1_df_dup_SEP_2020_women_miss2<-
  CONS_C1_df_dup_SEP_2020_women_miss1 %>% 
  dplyr::left_join(num_otras_sus_mod_imputed[,c("amelia_fit_imputations_imp1_row","num_otras_sus_mod_imp")],
                   by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
  #si la edad al ingreso no existe, el valor promedio imutado es
  dplyr::mutate(num_otras_sus_mod= 
                  dplyr::case_when(is.na(num_otras_sus_mod)~num_otras_sus_mod_imp,
                                  T~as.character(num_otras_sus_mod))) %>% 
  dplyr::select(-num_otras_sus_mod_imp)

#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:

#is.na(edad_ini_cons) & is.na(edad_ini_sus_prin) & is.na(min_edad_al_ing)~as.numeric(avg),
#table(is.na(CONS_C1_df_dup_SEP_2020_women_miss1$edad_ini_cons))
paste0("Number of rows with values that did not fulfilled the conditions: ",CONS_C1_df_dup_SEP_2020_women_miss2 %>%  dplyr::filter(is.na(num_otras_sus_mod)) %>% 
    dplyr::select(hash_key, edad_al_ing_grupos,num_otras_sus_mod) %>% nrow())
## [1] "Number of rows with values that did not fulfilled the conditions: 0"
#Lo importante es tener en cuenta que las imputaciones se hicieron por filas; no, en cambio, ahora debemos reemplazar aquellos casos que tienen perdidos (no cumplieron con las condiciones) con el valor mínimo
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss2)-nrow(CONS_C1_df_dup_SEP_2020_women_miss1)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were 0 values of co-occurring SUDs available.


Frequency of Use of the Primary Substance at Admission

Another variable that is worth imputing is the Frequency of use of primary drug at admission (n= 116). In case of ties, we selected the imputed values with the value with the most frequent drug use.


# Ver distintos valores propuestos para sustancia de inciio
freq_cons_sus_prin_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
       amelia_fit$imputations$imp1$freq_cons_sus_prin,
       amelia_fit$imputations$imp2$freq_cons_sus_prin,
       amelia_fit$imputations$imp3$freq_cons_sus_prin,
       amelia_fit$imputations$imp4$freq_cons_sus_prin,
       amelia_fit$imputations$imp5$freq_cons_sus_prin,
       amelia_fit$imputations$imp6$freq_cons_sus_prin,
       amelia_fit$imputations$imp7$freq_cons_sus_prin,
       amelia_fit$imputations$imp8$freq_cons_sus_prin,
       amelia_fit$imputations$imp9$freq_cons_sus_prin,
       amelia_fit$imputations$imp10$freq_cons_sus_prin,
       amelia_fit$imputations$imp11$freq_cons_sus_prin,
       amelia_fit$imputations$imp12$freq_cons_sus_prin,
       amelia_fit$imputations$imp13$freq_cons_sus_prin,
       amelia_fit$imputations$imp14$freq_cons_sus_prin,
       amelia_fit$imputations$imp15$freq_cons_sus_prin,
       amelia_fit$imputations$imp16$freq_cons_sus_prin,
       amelia_fit$imputations$imp17$freq_cons_sus_prin,
       amelia_fit$imputations$imp18$freq_cons_sus_prin,
       amelia_fit$imputations$imp19$freq_cons_sus_prin,
       amelia_fit$imputations$imp20$freq_cons_sus_prin,
       amelia_fit$imputations$imp21$freq_cons_sus_prin,
       amelia_fit$imputations$imp22$freq_cons_sus_prin,
       amelia_fit$imputations$imp23$freq_cons_sus_prin,
       amelia_fit$imputations$imp24$freq_cons_sus_prin,
       amelia_fit$imputations$imp25$freq_cons_sus_prin,
       amelia_fit$imputations$imp26$freq_cons_sus_prin,
       amelia_fit$imputations$imp27$freq_cons_sus_prin,
       amelia_fit$imputations$imp28$freq_cons_sus_prin,
       amelia_fit$imputations$imp29$freq_cons_sus_prin,
       amelia_fit$imputations$imp30$freq_cons_sus_prin,
       amelia_fit$imputations$imp31$freq_cons_sus_prin,
       amelia_fit$imputations$imp32$freq_cons_sus_prin,
       amelia_fit$imputations$imp33$freq_cons_sus_prin,
       amelia_fit$imputations$imp34$freq_cons_sus_prin,
       amelia_fit$imputations$imp35$freq_cons_sus_prin,
       amelia_fit$imputations$imp36$freq_cons_sus_prin,
       amelia_fit$imputations$imp37$freq_cons_sus_prin,
       amelia_fit$imputations$imp38$freq_cons_sus_prin,
       amelia_fit$imputations$imp39$freq_cons_sus_prin,
       amelia_fit$imputations$imp40$freq_cons_sus_prin,
       amelia_fit$imputations$imp41$freq_cons_sus_prin,
       amelia_fit$imputations$imp42$freq_cons_sus_prin,
       amelia_fit$imputations$imp43$freq_cons_sus_prin,
       amelia_fit$imputations$imp44$freq_cons_sus_prin,
       amelia_fit$imputations$imp45$freq_cons_sus_prin,
       amelia_fit$imputations$imp46$freq_cons_sus_prin,
       amelia_fit$imputations$imp47$freq_cons_sus_prin,
       amelia_fit$imputations$imp48$freq_cons_sus_prin,
       amelia_fit$imputations$imp49$freq_cons_sus_prin,
       amelia_fit$imputations$imp50$freq_cons_sus_prin,
       amelia_fit$imputations$imp51$freq_cons_sus_prin,
       amelia_fit$imputations$imp52$freq_cons_sus_prin,
       amelia_fit$imputations$imp53$freq_cons_sus_prin,
       amelia_fit$imputations$imp54$freq_cons_sus_prin,
       amelia_fit$imputations$imp55$freq_cons_sus_prin,
       amelia_fit$imputations$imp56$freq_cons_sus_prin,
       amelia_fit$imputations$imp57$freq_cons_sus_prin,
       amelia_fit$imputations$imp58$freq_cons_sus_prin,
       amelia_fit$imputations$imp59$freq_cons_sus_prin,
       amelia_fit$imputations$imp60$freq_cons_sus_prin,
       amelia_fit$imputations$imp61$freq_cons_sus_prin
       ) 

freq_cons_sus_prin_imputed<-
freq_cons_sus_prin_imputed %>% 
  data.frame() %>% 
dplyr::mutate(across(c(amelia_fit.imputations.imp1.freq_cons_sus_prin:amelia_fit.imputations.imp30.freq_cons_sus_prin),~dplyr::case_when(grepl("1 day a week or more",as.character(.))~1,TRUE~0), .names="1_day_{col}"))%>%
dplyr::mutate(across(c(amelia_fit.imputations.imp1.freq_cons_sus_prin:amelia_fit.imputations.imp30.freq_cons_sus_prin),~dplyr::case_when(grepl("2 to 3 days a week",as.character(.))~1,TRUE~0), .names="2_3_{col}"))%>%
dplyr::mutate(across(c(amelia_fit.imputations.imp1.freq_cons_sus_prin:amelia_fit.imputations.imp30.freq_cons_sus_prin),~dplyr::case_when(grepl("4 to 6 days a week",as.character(.))~1,TRUE~0), .names="4_6_{col}"))%>%
  dplyr::mutate(across(c(amelia_fit.imputations.imp1.freq_cons_sus_prin:amelia_fit.imputations.imp30.freq_cons_sus_prin),~dplyr::case_when(grepl("Less than 1 day a week",as.character(.))~1,TRUE~0), .names="less_1_{col}"))%>%
  dplyr::mutate(across(c(amelia_fit.imputations.imp1.freq_cons_sus_prin:amelia_fit.imputations.imp30.freq_cons_sus_prin),~dplyr::case_when(grepl("Did not use",as.character(.))~1,TRUE~0), .names="did_not_{col}"))%>%
    dplyr::mutate(across(c(amelia_fit.imputations.imp1.freq_cons_sus_prin:amelia_fit.imputations.imp30.freq_cons_sus_prin),~dplyr::case_when(grepl("Daily",as.character(.))~1,TRUE~0), .names="daily_{col}"))%>%
  dplyr::mutate(freq_cons_sus_prin_daily = base::rowSums(dplyr::select(., starts_with("daily_")))) %>% 
  dplyr::mutate(freq_cons_sus_prin_4_6 = base::rowSums(dplyr::select(., starts_with("4_6_"))))%>%
  dplyr::mutate(freq_cons_sus_prin_2_3 = base::rowSums(dplyr::select(., starts_with("2_3_"))))%>%
  dplyr::mutate(freq_cons_sus_prin_1_day = base::rowSums(dplyr::select(., starts_with("1_day_"))))%>%
  dplyr::mutate(freq_cons_sus_prin_less_1 = base::rowSums(dplyr::select(., starts_with("less_1_"))))%>%
  dplyr::mutate(freq_cons_sus_prin_did_not = base::rowSums(dplyr::select(., starts_with("did_not_")))) %>% 
  #dplyr::summarise(min_mar=max(sus_ini_mod_mvv_mar[sus_ini_mod_mvv_mar<30]),min_oh=max(sus_ini_mod_mvv_oh[sus_ini_mod_mvv_oh<30]),min_pb=max(sus_ini_mod_mvv_pb[sus_ini_mod_mvv_pb<30]),min_coc=max(sus_ini_mod_mvv_coc[sus_ini_mod_mvv_coc<30]),min_otr=max(sus_ini_mod_mvv_otr[sus_ini_mod_mvv_otr<30]))
  dplyr::mutate(freq_cons_sus_prin_tot=dplyr::case_when(freq_cons_sus_prin_1_day>0~1,TRUE~0)) %>% 
  dplyr::mutate(freq_cons_sus_prin_tot=dplyr::case_when(freq_cons_sus_prin_2_3>0~freq_cons_sus_prin_tot+1,TRUE~freq_cons_sus_prin_tot)) %>% 
  dplyr::mutate(freq_cons_sus_prin_tot=dplyr::case_when(freq_cons_sus_prin_4_6>0~freq_cons_sus_prin_tot+1,TRUE~freq_cons_sus_prin_tot)) %>% 
  dplyr::mutate(freq_cons_sus_prin_tot=dplyr::case_when(freq_cons_sus_prin_less_1>0~freq_cons_sus_prin_tot+1,TRUE~freq_cons_sus_prin_tot)) %>% 
  dplyr::mutate(freq_cons_sus_prin_tot=dplyr::case_when(freq_cons_sus_prin_did_not>0~freq_cons_sus_prin_tot+1,TRUE~freq_cons_sus_prin_tot)) %>% 
  dplyr::mutate(freq_cons_sus_prin_tot=dplyr::case_when(freq_cons_sus_prin_daily>0~freq_cons_sus_prin_tot+1,TRUE~freq_cons_sus_prin_tot)) %>% 
  #hierarchy
  dplyr::mutate(freq_cons_sus_prin_to_imputation=
                  dplyr::case_when(freq_cons_sus_prin_tot==1 & freq_cons_sus_prin_daily>0~"Daily",
                                     freq_cons_sus_prin_tot==1 & freq_cons_sus_prin_4_6>0~"4 to 6 days a week",freq_cons_sus_prin_tot==1 & freq_cons_sus_prin_2_3>0~"2 to 3 days a week",freq_cons_sus_prin_tot==1 & freq_cons_sus_prin_1_day>0~"1 day a week or more",freq_cons_sus_prin_tot==1 & freq_cons_sus_prin_less_1>0~"Less than 1 day a week",freq_cons_sus_prin_tot==1 & freq_cons_sus_prin_did_not>0~"Did not use",freq_cons_sus_prin_tot>1 & freq_cons_sus_prin_daily>0~"Daily",freq_cons_sus_prin_tot>1 & freq_cons_sus_prin_4_6>0~"4 to 6 days a week",freq_cons_sus_prin_tot>1 & freq_cons_sus_prin_2_3>0~"2 to 3 days a week",freq_cons_sus_prin_tot>1 & freq_cons_sus_prin_1_day>0~"1 day a week or more",freq_cons_sus_prin_tot>1 & freq_cons_sus_prin_less_1>0~"Less than 1 day a week",freq_cons_sus_prin_tot>1 & freq_cons_sus_prin_did_not>0~"Did not use")) %>% 
  janitor::clean_names()

freq_cons_sus_prin_imputed<-
dplyr::select(freq_cons_sus_prin_imputed,amelia_fit_imputations_imp1_row,freq_cons_sus_prin_to_imputation)

#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:

CONS_C1_df_dup_SEP_2020_women_miss3<-
CONS_C1_df_dup_SEP_2020_women_miss2 %>% 
   dplyr::left_join(freq_cons_sus_prin_imputed, by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(freq_cons_sus_prin=factor(dplyr::case_when(is.na(freq_cons_sus_prin)~as.character(freq_cons_sus_prin_to_imputation), TRUE~as.character(freq_cons_sus_prin)))) %>% 
  dplyr::select(-freq_cons_sus_prin_to_imputation) %>% 
  data.table()
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss3)-nrow(CONS_C1_df_dup_SEP_2020_women_miss2)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were no missing values.


Educational Attainment

Another variable that is worth imputing is the Educational Attainment (n= 115). In case of ties,w imputed for the most vulnerable category among the candidates.


# Ver distintos valores propuestos para sustancia de inciio
escolaridad_rec_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
                  amelia_fit$imputations$imp1$escolaridad_rec,
                  amelia_fit$imputations$imp2$escolaridad_rec,
                  amelia_fit$imputations$imp3$escolaridad_rec,
                  amelia_fit$imputations$imp4$escolaridad_rec,
                  amelia_fit$imputations$imp5$escolaridad_rec,
                  amelia_fit$imputations$imp6$escolaridad_rec,
                  amelia_fit$imputations$imp7$escolaridad_rec,
                  amelia_fit$imputations$imp8$escolaridad_rec,
                  amelia_fit$imputations$imp9$escolaridad_rec,
                  amelia_fit$imputations$imp10$escolaridad_rec,
                  amelia_fit$imputations$imp11$escolaridad_rec,
                  amelia_fit$imputations$imp12$escolaridad_rec,
                  amelia_fit$imputations$imp13$escolaridad_rec,
                  amelia_fit$imputations$imp14$escolaridad_rec,
                  amelia_fit$imputations$imp15$escolaridad_rec,
                  amelia_fit$imputations$imp16$escolaridad_rec,
                  amelia_fit$imputations$imp17$escolaridad_rec,
                  amelia_fit$imputations$imp18$escolaridad_rec,
                  amelia_fit$imputations$imp19$escolaridad_rec,
                  amelia_fit$imputations$imp20$escolaridad_rec,
                  amelia_fit$imputations$imp21$escolaridad_rec,
                  amelia_fit$imputations$imp22$escolaridad_rec,
                  amelia_fit$imputations$imp23$escolaridad_rec,
                  amelia_fit$imputations$imp24$escolaridad_rec,
                  amelia_fit$imputations$imp25$escolaridad_rec,
                  amelia_fit$imputations$imp26$escolaridad_rec,
                  amelia_fit$imputations$imp27$escolaridad_rec,
                  amelia_fit$imputations$imp28$escolaridad_rec,
                  amelia_fit$imputations$imp29$escolaridad_rec,
                  amelia_fit$imputations$imp30$escolaridad_rec,
                  amelia_fit$imputations$imp31$escolaridad_rec,
                  amelia_fit$imputations$imp32$escolaridad_rec,
                  amelia_fit$imputations$imp33$escolaridad_rec,
                  amelia_fit$imputations$imp34$escolaridad_rec,
                  amelia_fit$imputations$imp35$escolaridad_rec,
                  amelia_fit$imputations$imp36$escolaridad_rec,
                  amelia_fit$imputations$imp37$escolaridad_rec,
                  amelia_fit$imputations$imp38$escolaridad_rec,
                  amelia_fit$imputations$imp39$escolaridad_rec,
                  amelia_fit$imputations$imp40$escolaridad_rec,
                  amelia_fit$imputations$imp41$escolaridad_rec,
                  amelia_fit$imputations$imp42$escolaridad_rec,
                  amelia_fit$imputations$imp43$escolaridad_rec,
                  amelia_fit$imputations$imp44$escolaridad_rec,
                  amelia_fit$imputations$imp45$escolaridad_rec,
                  amelia_fit$imputations$imp46$escolaridad_rec,
                  amelia_fit$imputations$imp47$escolaridad_rec,
                  amelia_fit$imputations$imp48$escolaridad_rec,
                  amelia_fit$imputations$imp49$escolaridad_rec,
                  amelia_fit$imputations$imp50$escolaridad_rec,
                  amelia_fit$imputations$imp51$escolaridad_rec,
                  amelia_fit$imputations$imp52$escolaridad_rec,
                  amelia_fit$imputations$imp53$escolaridad_rec,
                  amelia_fit$imputations$imp54$escolaridad_rec,
                  amelia_fit$imputations$imp55$escolaridad_rec,
                  amelia_fit$imputations$imp56$escolaridad_rec,
                  amelia_fit$imputations$imp57$escolaridad_rec,
                  amelia_fit$imputations$imp58$escolaridad_rec,
                  amelia_fit$imputations$imp59$escolaridad_rec,
                  amelia_fit$imputations$imp60$escolaridad_rec) %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::ungroup() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>%
  # 1-Mild 2-Moderate   3-Severe 
  dplyr::summarise(primary_3=sum(value == "3-Completed primary school or less",na.rm=T),
                   high_sc_2=sum(value == "2-Completed high school or less",na.rm=T),
                  more_h_sc_1=sum(value =="1-More than high school",na.rm=T)) %>% 
  dplyr::ungroup() %>%
    dplyr::mutate(escolaridad_rec_imp= dplyr::case_when(
      (more_h_sc_1>primary_3) & (more_h_sc_1>high_sc_2)~"1-More than high school",
      (high_sc_2>primary_3) & (high_sc_2>more_h_sc_1)~"2-Completed high school or less",
      (primary_3>more_h_sc_1) & (primary_3>high_sc_2)~"3-Completed primary school or less"
      )) %>% 
#2) Resolve ties    
  dplyr::mutate(ties= dplyr::case_when(is.na(escolaridad_rec_imp)~1,T~0)) %>% 
  dplyr::mutate(escolaridad_rec_imp= dplyr::case_when(ties==1 & ((primary_3>more_h_sc_1)|(primary_3>high_sc_2))~"3-Completed primary school or less", ties==1 & ((high_sc_2>primary_3)|(high_sc_2>more_h_sc_1))~"2-Completed high school or less",
                T~escolaridad_rec_imp))
## `summarise()` ungrouping output (override with `.groups` argument)
#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(motivodeegreso_mod_imp,evaluacindelprocesoteraputico)

CONS_C1_df_dup_SEP_2020_women_miss4<-
CONS_C1_df_dup_SEP_2020_women_miss3 %>% 
   dplyr::left_join(escolaridad_rec_imputed[,c("amelia_fit_imputations_imp1_row","escolaridad_rec_imp")], by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(escolaridad_rec=factor(dplyr::case_when(is.na(escolaridad_rec)~ escolaridad_rec_imp,
                                                                        TRUE~as.character(escolaridad_rec)))) %>% 
     dplyr::mutate(escolaridad_rec=parse_factor(as.character(escolaridad_rec),levels=c("1-More than high school", "2-Completed high school or less","3-Completed primary school or less"), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "UTF-8"))) %>% 
  dplyr::select(-escolaridad_rec_imp) %>% 
  data.table()
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss4)-nrow(CONS_C1_df_dup_SEP_2020_women_miss3)>0){
  warning("AGS: Some rows were added in the imputation")}


We ended having 0 missing values in educational attainment.


Marital status

Additionally, we replaced missing values of the marital status (n=34). Since different marital status were not clearly more vulnerable between each other, we selected the most frequent imputed value among the different imputed databases. Only in case of ties in the candidate values, we resolved them by discarding “married” status, which could be somehow less vulnerable than other categories.


# Ver distintos valores propuestos para estado conyugal
estado_conyugal_2_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
       amelia_fit$imputations$imp1$estado_conyugal_2,
       amelia_fit$imputations$imp2$estado_conyugal_2,
       amelia_fit$imputations$imp3$estado_conyugal_2,
       amelia_fit$imputations$imp4$estado_conyugal_2,
       amelia_fit$imputations$imp5$estado_conyugal_2,
       amelia_fit$imputations$imp6$estado_conyugal_2,
       amelia_fit$imputations$imp7$estado_conyugal_2,
       amelia_fit$imputations$imp8$estado_conyugal_2,
       amelia_fit$imputations$imp9$estado_conyugal_2,
       amelia_fit$imputations$imp10$estado_conyugal_2,
       amelia_fit$imputations$imp11$estado_conyugal_2,
       amelia_fit$imputations$imp12$estado_conyugal_2,
       amelia_fit$imputations$imp13$estado_conyugal_2,
       amelia_fit$imputations$imp14$estado_conyugal_2,
       amelia_fit$imputations$imp15$estado_conyugal_2,
       amelia_fit$imputations$imp16$estado_conyugal_2,
       amelia_fit$imputations$imp17$estado_conyugal_2,
       amelia_fit$imputations$imp18$estado_conyugal_2,
       amelia_fit$imputations$imp19$estado_conyugal_2,
       amelia_fit$imputations$imp20$estado_conyugal_2,
       amelia_fit$imputations$imp21$estado_conyugal_2,
       amelia_fit$imputations$imp22$estado_conyugal_2,
       amelia_fit$imputations$imp23$estado_conyugal_2,
       amelia_fit$imputations$imp24$estado_conyugal_2,
       amelia_fit$imputations$imp25$estado_conyugal_2,
       amelia_fit$imputations$imp26$estado_conyugal_2,
       amelia_fit$imputations$imp27$estado_conyugal_2,
       amelia_fit$imputations$imp28$estado_conyugal_2,
       amelia_fit$imputations$imp29$estado_conyugal_2,
       amelia_fit$imputations$imp30$estado_conyugal_2,
       amelia_fit$imputations$imp31$estado_conyugal_2,
       amelia_fit$imputations$imp32$estado_conyugal_2,
       amelia_fit$imputations$imp33$estado_conyugal_2,
       amelia_fit$imputations$imp34$estado_conyugal_2,
       amelia_fit$imputations$imp35$estado_conyugal_2,
       amelia_fit$imputations$imp36$estado_conyugal_2,
       amelia_fit$imputations$imp37$estado_conyugal_2,
       amelia_fit$imputations$imp38$estado_conyugal_2,
       amelia_fit$imputations$imp39$estado_conyugal_2,
       amelia_fit$imputations$imp40$estado_conyugal_2,
       amelia_fit$imputations$imp41$estado_conyugal_2,
       amelia_fit$imputations$imp42$estado_conyugal_2,
       amelia_fit$imputations$imp43$estado_conyugal_2,
       amelia_fit$imputations$imp44$estado_conyugal_2,
       amelia_fit$imputations$imp45$estado_conyugal_2,
       amelia_fit$imputations$imp46$estado_conyugal_2,
       amelia_fit$imputations$imp47$estado_conyugal_2,
       amelia_fit$imputations$imp48$estado_conyugal_2,
       amelia_fit$imputations$imp49$estado_conyugal_2,
       amelia_fit$imputations$imp50$estado_conyugal_2,
       amelia_fit$imputations$imp51$estado_conyugal_2,
       amelia_fit$imputations$imp52$estado_conyugal_2,
       amelia_fit$imputations$imp53$estado_conyugal_2,
       amelia_fit$imputations$imp54$estado_conyugal_2,
       amelia_fit$imputations$imp55$estado_conyugal_2,
       amelia_fit$imputations$imp56$estado_conyugal_2,
       amelia_fit$imputations$imp57$estado_conyugal_2,
       amelia_fit$imputations$imp58$estado_conyugal_2,
       amelia_fit$imputations$imp59$estado_conyugal_2,
       amelia_fit$imputations$imp60$estado_conyugal_2,
       amelia_fit$imputations$imp61$estado_conyugal_2
       ) 

estado_conyugal_2_imputed<-
estado_conyugal_2_imputed %>% 
  data.frame() %>% 
dplyr::mutate(across(c(amelia_fit.imputations.imp1.estado_conyugal_2:amelia_fit.imputations.imp30.estado_conyugal_2),~dplyr::case_when(grepl("Married/Shared living arrangements",as.character(.))~1,TRUE~0), .names="married_{col}"))%>%
dplyr::mutate(across(c(amelia_fit.imputations.imp1.estado_conyugal_2:amelia_fit.imputations.imp30.estado_conyugal_2),~dplyr::case_when(grepl("Separated/Divorced",as.character(.))~1,TRUE~0), .names="sep_div_{col}"))%>%
dplyr::mutate(across(c(amelia_fit.imputations.imp1.estado_conyugal_2:amelia_fit.imputations.imp30.estado_conyugal_2),~dplyr::case_when(grepl("Single",as.character(.))~1,TRUE~0), .names="singl_{col}"))%>%
  dplyr::mutate(across(c(amelia_fit.imputations.imp1.estado_conyugal_2:amelia_fit.imputations.imp30.estado_conyugal_2),~dplyr::case_when(grepl("Widower",as.character(.))~1,TRUE~0), .names="widow_{col}"))%>%
 
  dplyr::mutate(estado_conyugal_2_married = base::rowSums(dplyr::select(., starts_with("married_"))))%>%
  dplyr::mutate(estado_conyugal_2_sep_div = base::rowSums(dplyr::select(., starts_with("sep_div_"))))%>%
  dplyr::mutate(estado_conyugal_2_singl = base::rowSums(dplyr::select(., starts_with("singl_"))))%>%
  dplyr::mutate(estado_conyugal_2_wid = base::rowSums(dplyr::select(., starts_with("widow_"))))%>%
  #dplyr::summarise(min_mar=max(sus_ini_mod_mvv_mar[sus_ini_mod_mvv_mar<30]),min_oh=max(sus_ini_mod_mvv_oh[sus_ini_mod_mvv_oh<30]),min_pb=max(sus_ini_mod_mvv_pb[sus_ini_mod_mvv_pb<30]),min_coc=max(sus_ini_mod_mvv_coc[sus_ini_mod_mvv_coc<30]),min_otr=max(sus_ini_mod_mvv_otr[sus_ini_mod_mvv_otr<30]))
  dplyr::mutate(estado_conyugal_2_tot=dplyr::case_when(estado_conyugal_2_married>0~1,TRUE~0)) %>% 
  dplyr::mutate(estado_conyugal_2_tot=dplyr::case_when(estado_conyugal_2_sep_div>0~estado_conyugal_2_tot+1,TRUE~estado_conyugal_2_tot)) %>% 
  dplyr::mutate(estado_conyugal_2_tot=dplyr::case_when(estado_conyugal_2_singl>0~estado_conyugal_2_tot+1,TRUE~estado_conyugal_2_tot)) %>% 
  dplyr::mutate(estado_conyugal_2_tot=dplyr::case_when(estado_conyugal_2_wid>0~estado_conyugal_2_tot+1,TRUE~estado_conyugal_2_tot)) %>% 
  janitor::clean_names()
  
estado_conyugal_2_imputed_cat_est_cony<-  
    estado_conyugal_2_imputed %>%
        tidyr::pivot_longer(c(estado_conyugal_2_married, estado_conyugal_2_sep_div, estado_conyugal_2_singl, estado_conyugal_2_wid), names_to = "cat_est_conyugal", values_to = "count") %>%
        dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
        dplyr::mutate(estado_conyugal_2_imputed_max=max(count,na.rm=T)) %>% 
        dplyr::ungroup() %>% 
        dplyr::filter(estado_conyugal_2_imputed_max==count) %>% 
        dplyr::select(amelia_fit_imputations_imp1_row,cat_est_conyugal,count) %>% 
        dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
        dplyr::mutate(n_row=n()) %>% 
        dplyr::ungroup() %>% 
        dplyr::mutate(cat_est_conyugal=dplyr::case_when(n_row>1~NA_character_,
                                                        TRUE~cat_est_conyugal)) %>% 
        dplyr::distinct(amelia_fit_imputations_imp1_row,.keep_all = T)
  
estado_conyugal_2_imputed<-
  estado_conyugal_2_imputed %>% 
    dplyr::left_join(estado_conyugal_2_imputed_cat_est_cony, by="amelia_fit_imputations_imp1_row") %>%
    dplyr::mutate(cat_est_conyugal=dplyr::case_when(cat_est_conyugal=="estado_conyugal_2_married"~"Married/Shared living arrangements",cat_est_conyugal=="estado_conyugal_2_sep_div"~"Separated/Divorced",cat_est_conyugal=="estado_conyugal_2_singl"~"Single",cat_est_conyugal=="estado_conyugal_2_wid"~"Widower"
    ))%>% 
  janitor::clean_names()

#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:

CONS_C1_df_dup_SEP_2020_women_miss5_prev<-
CONS_C1_df_dup_SEP_2020_women_miss4 %>% 
   dplyr::left_join(dplyr::select(estado_conyugal_2_imputed,amelia_fit_imputations_imp1_row,cat_est_conyugal), by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(estado_conyugal_2=factor(dplyr::case_when(is.na(estado_conyugal_2)~as.character(cat_est_conyugal),TRUE~as.character(estado_conyugal_2)))) %>% 
  dplyr::select(-cat_est_conyugal) %>% 
  data.table()

# casos problemáticos de matrimonio c(59664, 17582, 161721, 36520)

no_calzaron_estado_cony<-
CONS_C1_df_dup_SEP_2020_women_miss5_prev %>% dplyr::filter(is.na(estado_conyugal_2)) %>% dplyr::distinct(row) %>% unlist()

estado_conyugal_2_imputed2<-
estado_conyugal_2_imputed %>% 
     dplyr::filter(amelia_fit_imputations_imp1_row %in%  no_calzaron_estado_cony) %>% 
  dplyr::select(amelia_fit_imputations_imp1_row, estado_conyugal_2_married, estado_conyugal_2_sep_div,estado_conyugal_2_singl, estado_conyugal_2_wid, estado_conyugal_2_tot, cat_est_conyugal) %>% 
  melt(id.vars="amelia_fit_imputations_imp1_row") %>% 
  dplyr::mutate(value=as.numeric(value)) %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::filter(value!="cat_est_conyugal") %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  slice_max(value, with_ties = T) %>% 
  dplyr::filter(variable!="estado_conyugal_2_married") %>% 
  dplyr::left_join(CONS_C1_df_dup_SEP_2020_women[,c("row","edad_al_ing")], by=c("amelia_fit_imputations_imp1_row"="row")) %>% 
  dplyr::mutate(value=dplyr::case_when(variable=="estado_conyugal_2_sep_div"~value*10,
                                       T~value)) %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  slice_max(value, with_ties = T) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(marital_status_imp=dplyr::case_when(grepl("_singl",variable)~"Single",
                grepl("_sep_div",variable)~"Separated/Divorced",
                grepl("_married",variable)~"Married/Shared living arrangements",
                grepl("_wid",variable)~"Widower"
                ))

#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#2nd round of imputation for ties
CONS_C1_df_dup_SEP_2020_women_miss5<-
CONS_C1_df_dup_SEP_2020_women_miss5_prev %>% 
   dplyr::left_join(dplyr::select(estado_conyugal_2_imputed2,amelia_fit_imputations_imp1_row,marital_status_imp), by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(estado_conyugal_2=factor(dplyr::case_when(is.na(estado_conyugal_2)~as.character(marital_status_imp),TRUE~as.character(estado_conyugal_2)))) %>% 
  dplyr::select(-marital_status_imp) %>% 
  data.table()

#CONS_C1_df_dup_SEP_2020_women_miss5 %>% 
#dplyr::filter(hash_key %in% CONS_C1_df_dup_SEP_2020_women_miss5 %>% dplyr::filter(is.na(estado_conyugal_2)) %>% dplyr::distinct(hash_key) %>% unlist())

if(nrow(CONS_C1_df_dup_SEP_2020_women_miss5)-nrow(CONS_C1_df_dup_SEP_2020_women_miss4)>0){
  warning("AGS: Some rows were added in the imputation")}


We could not resolve Marital status in 0 cases due to ties in the most frequent values.


Cause of Discharge

We looked over possible imputations to the truly missing values, discarding missing values due to censorship (n=3). In case of ties, we replace with the more vulnerable value.

motivo_de_egreso_a_imputar<-
CONS_C1_df_dup_SEP_2020_women_miss %>% dplyr::filter(is.na(motivodeegreso_mod_imp)) %>% dplyr::left_join(dplyr::select(CONS_C1_df_dup_SEP_2020,row,fech_egres_imp)) %>% dplyr::filter(!is.na(fech_egres_imp))%>%dplyr::select(row)
## Joining, by = "row"
#CONS_C1_df_dup_SEP_2020 %>% dplyr::filter(is.na(motivodeegreso_mod_imp)) %>% 
#    dplyr::select(row, hash_key, motivodeegreso_mod_imp, fech_egres_imp)
#    dplyr::filter(fech_egres_imp=="2019-11-13")

motivodeegreso_mod_imp_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
       amelia_fit$imputations$imp1$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp2$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp3$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp4$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp5$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp6$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp7$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp8$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp9$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp10$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp11$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp12$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp13$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp14$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp15$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp16$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp17$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp18$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp19$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp20$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp21$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp22$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp23$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp24$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp25$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp26$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp27$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp28$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp29$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp30$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp31$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp32$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp33$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp34$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp35$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp36$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp37$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp38$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp39$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp40$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp41$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp42$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp43$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp44$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp45$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp46$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp47$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp48$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp49$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp50$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp51$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp52$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp53$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp54$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp55$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp56$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp57$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp58$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp59$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp60$motivodeegreso_mod_imp,
       amelia_fit$imputations$imp61$motivodeegreso_mod_imp
       ) %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::ungroup() %>% 
  dplyr::filter(amelia_fit_imputations_imp1_row %in% unlist(motivo_de_egreso_a_imputar$row)) %>% 
  #FILTRAR CASOS QUE SON ILÓGICOS: MUERTES CON TRATAMIENTOS POSTERIORES (1)
  dplyr::left_join(dplyr::select(CONS_C1_df_dup_SEP_2020,row,motivodeegreso_mod_imp, fech_egres_imp,dup, duplicates_filtered,evaluacindelprocesoteraputico,fech_ing_next_treat),by=c("amelia_fit_imputations_imp1_row"="row")) %>% 
  dplyr::mutate(value_death=dplyr::case_when(value=="Death"& !is.na(fech_ing_next_treat)~1,T~0)) %>% 
  dplyr::filter(value_death!=1) %>%  
  #FILTRAR CASOS QUE SON ILÓGICOS: NO PUEDEN HABER TRATAMIENTOS EN CURSO CON TRATAMIENTOS POSTERIORES (2)
  dplyr::mutate(value_fail=dplyr::case_when(value=="Ongoing treatment"& !is.na(fech_ing_next_treat)~1,T~0)) %>% 
  dplyr::filter(value_fail!=1) %>%  
  #FILTRAR CASOS QUE SON ILÓGICOS: NO PUEDE HABER OTRA COSA QUE TRATAMIENTO EN CURSO CON FECHA DE CENSURA
  dplyr::mutate(value_ong=dplyr::case_when(value!="Ongoing treatment" & fech_egres_imp=="2019-11-13"~1,T~0)) %>% 
  dplyr::filter(value_ong!=1) %>%  
  #:#:#:#:#:
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  dplyr::summarise(adm_dis=sum(value == "Administrative discharge",na.rm=T),
                    death=sum(value == "Death",na.rm=T),
                    referral=sum(value == "Referral to another treatment",na.rm=T),
                    ther_dis=sum(value == "Therapeutic discharge",na.rm=T),
                    on_treat=sum(value == "Ongoing treatment",na.rm=T),
                    dropout=sum(value =="Drop-out",na.rm=T)) %>% 
  melt(id.vars="amelia_fit_imputations_imp1_row") %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  dplyr::slice_max(value) %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  dplyr::mutate(n=n()) %>% 
  dplyr::mutate(emp=dplyr::case_when(variable=="adm_dis" & n>1~1,T~0)) %>% 
  dplyr::filter(emp!=1) %>% 
  dplyr::mutate(motivodeegreso_mod_imp_imputation=
                  dplyr::case_when(variable=="adm_dis"~"Administrative discharge",
                                   variable=="death"~"Death",  
                                   variable=="ther_dis"~"Therapeutic discharge",
                                   variable=="on_treat"~"Ongoing treatment",
                                   variable=="referral"~"Referral to another treatment",
                                   variable=="dropout"~"Drop-out"))
## `summarise()` ungrouping output (override with `.groups` argument)
#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:
#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:
CONS_C1_df_dup_SEP_2020_women_miss6<-
CONS_C1_df_dup_SEP_2020_women_miss5 %>% 
   dplyr::left_join(motivodeegreso_mod_imp_imputed[,c("amelia_fit_imputations_imp1_row","motivodeegreso_mod_imp_imputation")], by=c("row"="amelia_fit_imputations_imp1_row")) %>%
  #dplyr::filter(is.na(motivodeegreso_mod_imp)) %>% dplyr::select(row,hash_key,motivodeegreso_mod_imp_original, motivodeegreso_mod_imp_imputation,motivodeegreso_mod_imp,fech_egres_num,fech_egres_imp)
  dplyr::left_join(cbind.data.frame(motivo_de_egreso_a_imputar,value_to_impute=1),"row") %>% 
  dplyr::mutate(motivodeegreso_mod_imp=factor(
     dplyr::case_when(is.na(motivodeegreso_mod_imp) & value_to_impute==1~motivodeegreso_mod_imp_imputation,
             T~as.character(motivodeegreso_mod_imp)))) %>% 
  dplyr::select(-motivodeegreso_mod_imp_imputation,-value_to_impute) %>% 
  data.table()
#CONS_C1_df_dup_SEP_2020_women_miss9 %>% janitor::tabyl(motivodeegreso_mod_imp,motivodeegreso_mod_imp_original)
#CONS_C1_df_dup_SEP_2020_women_miss9 %>% janitor::tabyl(motivodeegreso_mod_imp_original)

CONS_C1_df_dup_SEP_2020_women_miss6 %>% janitor::tabyl(motivodeegreso_mod_imp) %>%
    dplyr::mutate(percent=scales::percent(percent)) %>%  
    knitr::kable(.,format = "html", format.args = list(decimal.mark = ".", big.mark = ","),
               caption = paste0("Table 2. Imputed Cause of Discharge vs. Original Cause of Discharge"),
               #col.names = c("Cause of Discharge","1-High Achievement", "2- Medium Achievement","3- Minimum Achievement","Null Values"),
               align =rep('c', 101)) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::add_footnote("Note. NA= Null values", notation="none") %>% 
  kableExtra::scroll_box(width = "100%", height = "375px") 
Table 2. Imputed Cause of Discharge vs. Original Cause of Discharge
motivodeegreso_mod_imp n percent
Administrative discharge 1,745 8.15%
Early Drop-out 3,165 14.77%
Late Drop-out 7,108 33.18%
Ongoing treatment 1,601 7.47%
Referral to another treatment 2,676 12.49%
Therapeutic discharge 5,128 23.94%
Note. NA= Null values
#

if(nrow(CONS_C1_df_dup_SEP_2020_women_miss6)-nrow(CONS_C1_df_dup_SEP_2020_women_miss5)>0){
  warning("AGS: Some rows were added in the imputation")}


As a result of the imputations, there were no missing values.


Biopsychosocial involvement

Another variable that is worth imputing is the Biopsychosocial involvement (n= 371). In case of ties, we selected the imputed values with the value with the minimum involvement. In case of ties, we chose the most vulnerable value.


# Ver distintos valores propuestos para sustancia de inciio

#No se ve un patrón de dependencia entre el compromiso biopsicosocial y el estatus de egreso
#  table(CONS_C1_df_dup_SEP_2020_women_miss$compromiso_biopsicosocial,
#       CONS_C1_df_dup_SEP_2020_women_miss$motivodeegreso_mod_imp)

comp_biopsisoc_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
         amelia_fit$imputations$imp1$compromiso_biopsicosocial,
       amelia_fit$imputations$imp2$compromiso_biopsicosocial,
       amelia_fit$imputations$imp3$compromiso_biopsicosocial,
       amelia_fit$imputations$imp4$compromiso_biopsicosocial,
       amelia_fit$imputations$imp5$compromiso_biopsicosocial,
       amelia_fit$imputations$imp6$compromiso_biopsicosocial,
       amelia_fit$imputations$imp7$compromiso_biopsicosocial,
       amelia_fit$imputations$imp8$compromiso_biopsicosocial,
       amelia_fit$imputations$imp9$compromiso_biopsicosocial,
       amelia_fit$imputations$imp10$compromiso_biopsicosocial,
       amelia_fit$imputations$imp11$compromiso_biopsicosocial,
       amelia_fit$imputations$imp12$compromiso_biopsicosocial,
       amelia_fit$imputations$imp13$compromiso_biopsicosocial,
       amelia_fit$imputations$imp14$compromiso_biopsicosocial,
       amelia_fit$imputations$imp15$compromiso_biopsicosocial,
       amelia_fit$imputations$imp16$compromiso_biopsicosocial,
       amelia_fit$imputations$imp17$compromiso_biopsicosocial,
       amelia_fit$imputations$imp18$compromiso_biopsicosocial,
       amelia_fit$imputations$imp19$compromiso_biopsicosocial,
       amelia_fit$imputations$imp20$compromiso_biopsicosocial,
       amelia_fit$imputations$imp21$compromiso_biopsicosocial,
       amelia_fit$imputations$imp22$compromiso_biopsicosocial,
       amelia_fit$imputations$imp23$compromiso_biopsicosocial,
       amelia_fit$imputations$imp24$compromiso_biopsicosocial,
       amelia_fit$imputations$imp25$compromiso_biopsicosocial,
       amelia_fit$imputations$imp26$compromiso_biopsicosocial,
       amelia_fit$imputations$imp27$compromiso_biopsicosocial,
       amelia_fit$imputations$imp28$compromiso_biopsicosocial,
       amelia_fit$imputations$imp29$compromiso_biopsicosocial,
       amelia_fit$imputations$imp30$compromiso_biopsicosocial,
       amelia_fit$imputations$imp31$compromiso_biopsicosocial,
       amelia_fit$imputations$imp32$compromiso_biopsicosocial,
       amelia_fit$imputations$imp33$compromiso_biopsicosocial,
       amelia_fit$imputations$imp34$compromiso_biopsicosocial,
       amelia_fit$imputations$imp35$compromiso_biopsicosocial,
       amelia_fit$imputations$imp36$compromiso_biopsicosocial,
       amelia_fit$imputations$imp37$compromiso_biopsicosocial,
       amelia_fit$imputations$imp38$compromiso_biopsicosocial,
       amelia_fit$imputations$imp39$compromiso_biopsicosocial,
       amelia_fit$imputations$imp40$compromiso_biopsicosocial,
       amelia_fit$imputations$imp41$compromiso_biopsicosocial,
       amelia_fit$imputations$imp42$compromiso_biopsicosocial,
       amelia_fit$imputations$imp43$compromiso_biopsicosocial,
       amelia_fit$imputations$imp44$compromiso_biopsicosocial,
       amelia_fit$imputations$imp45$compromiso_biopsicosocial,
       amelia_fit$imputations$imp46$compromiso_biopsicosocial,
       amelia_fit$imputations$imp47$compromiso_biopsicosocial,
       amelia_fit$imputations$imp48$compromiso_biopsicosocial,
       amelia_fit$imputations$imp49$compromiso_biopsicosocial,
       amelia_fit$imputations$imp50$compromiso_biopsicosocial,
       amelia_fit$imputations$imp51$compromiso_biopsicosocial,
       amelia_fit$imputations$imp52$compromiso_biopsicosocial,
       amelia_fit$imputations$imp53$compromiso_biopsicosocial,
       amelia_fit$imputations$imp54$compromiso_biopsicosocial,
       amelia_fit$imputations$imp55$compromiso_biopsicosocial,
       amelia_fit$imputations$imp56$compromiso_biopsicosocial,
       amelia_fit$imputations$imp57$compromiso_biopsicosocial,
       amelia_fit$imputations$imp58$compromiso_biopsicosocial,
       amelia_fit$imputations$imp59$compromiso_biopsicosocial,
       amelia_fit$imputations$imp60$compromiso_biopsicosocial,
       amelia_fit$imputations$imp61$compromiso_biopsicosocial
       ) %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::arrange(amelia_fit_imputations_imp1_row) %>% 
  dplyr::ungroup() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>%
  # 1-Mild 2-Moderate   3-Severe 
  dplyr::summarise(severe_3=sum(value == "3-Severe",na.rm=T),
                   mod_2=sum(value == "2-Moderate",na.rm=T),
                  mild_1=sum(value =="1-Mild",na.rm=T)) %>% 
  dplyr::ungroup() %>%
    dplyr::mutate(comp_biopsisoc_imp= dplyr::case_when(
      (severe_3>mild_1) & (severe_3>mod_2)~"3-Severe",
      (mod_2>mild_1) & (mod_2>severe_3)~"2-Moderate",
      (mild_1>mod_2) & (mild_1>severe_3)~"1-Mild"
      )) %>% 
#2) Resolve ties    
  dplyr::mutate(ties= dplyr::case_when(is.na(comp_biopsisoc_imp)~1,T~0)) %>% 
  dplyr::mutate(comp_biopsisoc_imp= dplyr::case_when(ties==1 & ((severe_3>mod_2)|(severe_3>mild_1))~"3-Severe",
                                                     ties==1 & ((mod_2>mild_1)|(mod_2>severe_3))~"2-Moderate",
                T~comp_biopsisoc_imp))
## `summarise()` ungrouping output (override with `.groups` argument)
#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:
##
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(motivodeegreso_mod_imp,evaluacindelprocesoteraputico)

CONS_C1_df_dup_SEP_2020_women_miss7<-
CONS_C1_df_dup_SEP_2020_women_miss6 %>% 
   dplyr::left_join(comp_biopsisoc_imputed[,c("amelia_fit_imputations_imp1_row","comp_biopsisoc_imp")], by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(compromiso_biopsicosocial=factor(dplyr::case_when(is.na(compromiso_biopsicosocial) ~comp_biopsisoc_imp,
                                                                        TRUE~as.character(compromiso_biopsicosocial)))) %>% 
     dplyr::mutate(compromiso_biopsicosocial=parse_factor(as.character(compromiso_biopsicosocial),levels=c('1-Mild', '2-Moderate','3-Severe'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "UTF-8"))) %>% 
  dplyr::select(-comp_biopsisoc_imp) %>% 
  data.table()

if(nrow(CONS_C1_df_dup_SEP_2020_women_miss7)-nrow(CONS_C1_df_dup_SEP_2020_women_miss6)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were no missing values.


Tenure status of households

Another variable that is worth imputing is the Tenure status of households (n= 943). In case of ties, we selected the imputed values with the value with the minimum involvement. In case of ties, we kept what we thought was the most vulnerable value (discarding “Owner” or “Renting” values).


tenencia_de_la_vivienda_mod_imputed<-
 cbind.data.frame(amelia_fit$imputations$imp1$row,
         amelia_fit$imputations$imp1$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp2$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp3$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp4$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp5$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp6$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp7$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp8$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp9$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp10$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp11$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp12$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp13$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp14$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp15$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp16$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp17$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp18$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp19$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp20$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp21$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp22$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp23$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp24$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp25$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp26$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp27$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp28$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp29$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp30$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp31$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp32$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp33$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp34$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp35$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp36$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp37$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp38$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp39$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp40$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp41$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp42$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp43$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp44$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp45$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp46$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp47$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp48$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp49$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp50$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp51$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp52$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp53$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp54$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp55$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp56$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp57$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp58$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp59$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp60$tenencia_de_la_vivienda_mod,
       amelia_fit$imputations$imp61$tenencia_de_la_vivienda_mod
       ) %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row, value) %>% 
  tally() %>% 
  dplyr::ungroup() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  dplyr::top_n(1,n) %>% 
  dplyr::ungroup()

#tenencia_de_la_vivienda_mod_imputed %>% 
#  pivot_wider(id_cols="amelia_fit_imputations_imp1_row",names_from="value", values_from="n", values_fill=0) %>% 
#  dplyr::ungroup()

tenencia_de_la_vivienda_mod_imputed_dup<-
  tenencia_de_la_vivienda_mod_imputed %>% 
    dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
    dplyr::mutate(num=n()) %>% 
    dplyr::filter(num>1) %>% 
    dplyr::ungroup() %>% 
  #1) owner, discard if it is in the maximum
    dplyr::mutate(n=dplyr::case_when(value=="Owner/Transferred dwellings/Pays Dividends"~0,T~as.numeric(n))) %>% 
    dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
    dplyr::top_n(1,n) %>% 
    dplyr::ungroup() %>% 
    dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  #2) Renting vs. stays temporarily with a relative, keep the second
    dplyr::mutate(n=dplyr::case_when(value=="Renting"~0,T~as.numeric(n))) %>% 
    dplyr::top_n(1,n) %>% 
    dplyr::ungroup() %>% 
    dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
    dplyr::mutate(n_dup=n()) 

tenencia_de_la_vivienda_mod_imputed_final<-
tenencia_de_la_vivienda_mod_imputed %>% 
    dplyr::left_join(tenencia_de_la_vivienda_mod_imputed_dup, by=c("amelia_fit_imputations_imp1_row", "value")) %>% 
  #si es vacío, y no está en la base, es valor 0 (es difícil que)
    dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
    dplyr::mutate(sum= suppressWarnings(max(num, na.rm=T))) %>% 
    dplyr::ungroup() %>% 
  #descarto los que presentaron más de un valor para una misma fila y aquellos que no fueron seleccionados
    dplyr::mutate(descartar=dplyr::case_when(sum>1 & is.na(n.y)~1,T~0)) %>% 
    dplyr::filter(descartar==0)

ifelse(nrow(tenencia_de_la_vivienda_mod_imputed_final)/length(unique(CONS_C1_df_dup_SEP_2020_women_miss7$row))>1,
       "There are still more than one value in the imputation","")
## [1] ""
#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(motivodeegreso_mod_imp,evaluacindelprocesoteraputico)

CONS_C1_df_dup_SEP_2020_women_miss8<-
CONS_C1_df_dup_SEP_2020_women_miss7 %>% 
   dplyr::left_join(tenencia_de_la_vivienda_mod_imputed_final[,c("amelia_fit_imputations_imp1_row","value")], by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
    dplyr::mutate(tenencia_de_la_vivienda_mod=factor(dplyr::case_when(is.na(tenencia_de_la_vivienda_mod) ~value,
                                                                        TRUE~as.character(tenencia_de_la_vivienda_mod)))) %>% 
  dplyr::select(-value) %>% 
  data.table()
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss8)-nrow(CONS_C1_df_dup_SEP_2020_women_miss7)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were no missing values.


Number of children (max. Value) (Dichotomized)

A numeric variable that had a great proportion of missing values was this (n= 82).

As seen in the figure above, most of the imputations were around 1 and 3 children, leaving less space for an imputation of no children or more than 3. We imputed these values, by approximating the mean of the 61 candidate values to a discrete number.


numero_de_hijos_mod_rec_imputed<-
  cbind.data.frame(amelia_fit$imputations$imp1$row,
         amelia_fit$imputations$imp1$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp2$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp3$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp4$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp5$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp6$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp7$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp8$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp9$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp10$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp11$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp12$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp13$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp14$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp15$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp16$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp17$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp18$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp19$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp20$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp21$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp22$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp23$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp24$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp25$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp26$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp27$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp28$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp29$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp30$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp31$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp32$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp33$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp34$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp35$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp36$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp37$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp38$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp39$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp40$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp41$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp42$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp43$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp44$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp45$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp46$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp47$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp48$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp49$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp50$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp51$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp52$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp53$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp54$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp55$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp56$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp57$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp58$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp59$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp60$numero_de_hijos_mod_rec,
       amelia_fit$imputations$imp61$numero_de_hijos_mod_rec
       )  %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  dplyr::summarise(children= sum(value=="Yes"),
                   no_children= sum(value=="No")) %>% 
  dplyr::mutate(numero_de_hijos_mod_rec_imp=dplyr::case_when(children>=31~"Yes",
                                                    no_children>=31~"No"))

#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:

CONS_C1_df_dup_SEP_2020_women_miss9<-
CONS_C1_df_dup_SEP_2020_women_miss8 %>% 
    dplyr::left_join(dplyr::select(numero_de_hijos_mod_rec_imputed,amelia_fit_imputations_imp1_row,numero_de_hijos_mod_rec_imp), by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
  dplyr::mutate(numero_de_hijos_mod_rec=factor(dplyr::case_when(is.na(numero_de_hijos_mod_rec)~as.character(numero_de_hijos_mod_rec_imp),T~as.character(numero_de_hijos_mod_rec)))) %>%
  dplyr::select(-numero_de_hijos_mod_rec_imp) %>% 
  data.table()
#table(is.na(CONS_C1_df_dup_SEP_2020_women_miss12$numero_de_hijos_mod_rec))
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss9)-nrow(CONS_C1_df_dup_SEP_2020_women_miss8)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were no missing values.


Type of Program

A numeric variable that was important to impute missing values was the type of program (n= 17).


tipo_de_programa_2_imputed<-
  cbind.data.frame(amelia_fit$imputations$imp1$row,
         amelia_fit$imputations$imp1$tipo_de_programa_2,
       amelia_fit$imputations$imp2$tipo_de_programa_2,
       amelia_fit$imputations$imp3$tipo_de_programa_2,
       amelia_fit$imputations$imp4$tipo_de_programa_2,
       amelia_fit$imputations$imp5$tipo_de_programa_2,
       amelia_fit$imputations$imp6$tipo_de_programa_2,
       amelia_fit$imputations$imp7$tipo_de_programa_2,
       amelia_fit$imputations$imp8$tipo_de_programa_2,
       amelia_fit$imputations$imp9$tipo_de_programa_2,
       amelia_fit$imputations$imp10$tipo_de_programa_2,
       amelia_fit$imputations$imp11$tipo_de_programa_2,
       amelia_fit$imputations$imp12$tipo_de_programa_2,
       amelia_fit$imputations$imp13$tipo_de_programa_2,
       amelia_fit$imputations$imp14$tipo_de_programa_2,
       amelia_fit$imputations$imp15$tipo_de_programa_2,
       amelia_fit$imputations$imp16$tipo_de_programa_2,
       amelia_fit$imputations$imp17$tipo_de_programa_2,
       amelia_fit$imputations$imp18$tipo_de_programa_2,
       amelia_fit$imputations$imp19$tipo_de_programa_2,
       amelia_fit$imputations$imp20$tipo_de_programa_2,
       amelia_fit$imputations$imp21$tipo_de_programa_2,
       amelia_fit$imputations$imp22$tipo_de_programa_2,
       amelia_fit$imputations$imp23$tipo_de_programa_2,
       amelia_fit$imputations$imp24$tipo_de_programa_2,
       amelia_fit$imputations$imp25$tipo_de_programa_2,
       amelia_fit$imputations$imp26$tipo_de_programa_2,
       amelia_fit$imputations$imp27$tipo_de_programa_2,
       amelia_fit$imputations$imp28$tipo_de_programa_2,
       amelia_fit$imputations$imp29$tipo_de_programa_2,
       amelia_fit$imputations$imp30$tipo_de_programa_2,
       amelia_fit$imputations$imp31$tipo_de_programa_2,
       amelia_fit$imputations$imp32$tipo_de_programa_2,
       amelia_fit$imputations$imp33$tipo_de_programa_2,
       amelia_fit$imputations$imp34$tipo_de_programa_2,
       amelia_fit$imputations$imp35$tipo_de_programa_2,
       amelia_fit$imputations$imp36$tipo_de_programa_2,
       amelia_fit$imputations$imp37$tipo_de_programa_2,
       amelia_fit$imputations$imp38$tipo_de_programa_2,
       amelia_fit$imputations$imp39$tipo_de_programa_2,
       amelia_fit$imputations$imp40$tipo_de_programa_2,
       amelia_fit$imputations$imp41$tipo_de_programa_2,
       amelia_fit$imputations$imp42$tipo_de_programa_2,
       amelia_fit$imputations$imp43$tipo_de_programa_2,
       amelia_fit$imputations$imp44$tipo_de_programa_2,
       amelia_fit$imputations$imp45$tipo_de_programa_2,
       amelia_fit$imputations$imp46$tipo_de_programa_2,
       amelia_fit$imputations$imp47$tipo_de_programa_2,
       amelia_fit$imputations$imp48$tipo_de_programa_2,
       amelia_fit$imputations$imp49$tipo_de_programa_2,
       amelia_fit$imputations$imp50$tipo_de_programa_2,
       amelia_fit$imputations$imp51$tipo_de_programa_2,
       amelia_fit$imputations$imp52$tipo_de_programa_2,
       amelia_fit$imputations$imp53$tipo_de_programa_2,
       amelia_fit$imputations$imp54$tipo_de_programa_2,
       amelia_fit$imputations$imp55$tipo_de_programa_2,
       amelia_fit$imputations$imp56$tipo_de_programa_2,
       amelia_fit$imputations$imp57$tipo_de_programa_2,
       amelia_fit$imputations$imp58$tipo_de_programa_2,
       amelia_fit$imputations$imp59$tipo_de_programa_2,
       amelia_fit$imputations$imp60$tipo_de_programa_2,
       amelia_fit$imputations$imp61$tipo_de_programa_2
       )  %>% 
  melt(id.vars="amelia_fit$imputations$imp1$row") %>% 
  janitor::clean_names() %>% 
  dplyr::group_by(amelia_fit_imputations_imp1_row) %>% 
  dplyr::summarise(WE= sum(value=="Women specific"),
                   GP= sum(value=="General population")) %>% 
  dplyr::mutate(tipo_de_programa_2_imp=dplyr::case_when(WE>=31~"Women specific",
                                                    GP>=31~"General population"))

#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:#:#:#::#:#:#:

CONS_C1_df_dup_SEP_2020_women_miss10<-
CONS_C1_df_dup_SEP_2020_women_miss9 %>% 
    dplyr::left_join(dplyr::select(tipo_de_programa_2_imputed,amelia_fit_imputations_imp1_row,tipo_de_programa_2_imp), by=c("row"="amelia_fit_imputations_imp1_row")) %>% 
  dplyr::mutate(tipo_de_programa_2=factor(dplyr::case_when(is.na(tipo_de_programa_2)~as.character(tipo_de_programa_2_imp),T~as.character(tipo_de_programa_2)))) %>%
  dplyr::select(-tipo_de_programa_2_imp) %>% 
  data.table()
#table(is.na(CONS_C1_df_dup_SEP_2020_women_miss12$tipo_de_programa_2))
if(nrow(CONS_C1_df_dup_SEP_2020_women_miss10)-nrow(CONS_C1_df_dup_SEP_2020_women_miss9)>0){
  warning("AGS: Some rows were added in the imputation")}

As a result of the imputations, there were no missing values.


Sample Characteristics

We checked the characteristics of the sample depending on type of treatment (Residential or Outpatients).


#añado los imputados
CONS_C1_df_dup_SEP_2020_women_miss_after_imp<-
CONS_C1_df_dup_SEP_2020_women_miss10 %>% 
#  relocate(otras_sus1_mod, .after = last_col()) %>% 
  dplyr::left_join(dplyr::select(CONS_C1_df_dup_SEP_2020, row, fech_ing, fech_egres_imp, fech_ing_num, fech_egres_num, dias_treat_imp_sin_na,fech_ing_next_treat), by="row")%>% 
  #dplyr::filter(fech_egres_num==18213,!is.na(motivodeegreso_mod_imp)) %>% 
  dplyr::mutate(motivodeegreso_mod_imp=factor(dplyr::case_when(dias_treat_imp_sin_na>=90 & motivodeegreso_mod_imp=="Drop-out"~ "Late Drop-out",
                                                        dias_treat_imp_sin_na<90 & motivodeegreso_mod_imp=="Drop-out"~ "Early Drop-out",
                                                        fech_egres_num==18213 & is.na(motivodeegreso_mod_imp)~"Ongoing treatment",
                                                        TRUE~as.character(motivodeegreso_mod_imp)
                                                        ))) %>%
  dplyr::mutate(sum_miss = base::rowSums(is.na(dplyr::select(.,c(tipo_de_programa_2:motivodeegreso_mod_imp))))) %>% 
  dplyr::group_by(hash_key) %>% 
  dplyr::mutate(sum_miss=sum(sum_miss)) %>% 
  dplyr::ungroup() 

CONS_C1_df_dup_SEP_2020_women_miss_after_imp_descartados <-
  CONS_C1_df_dup_SEP_2020_women_miss_after_imp %>% 
  dplyr::filter(sum_miss>0)

CONS_C1_df_dup_SEP_2020_women_miss_after_imp_descartados %>% 
  rowwise %>%
  dplyr::mutate_at(.vars = vars(vector_variables),
                   .funs = ~ifelse(is.na(.), 1, 0)) %>% 
  dplyr::ungroup() %>% 
  dplyr::summarise_at(vars(vector_variables),~sum(.)) %>% 
  melt
## No id variables; using all as measure variables
##                       variable value
## 1           tipo_de_programa_2     0
## 2            estado_conyugal_2     0
## 3           edad_al_ing_grupos     0
## 4              escolaridad_rec     0
## 5            sus_principal_mod     0
## 6           freq_cons_sus_prin     0
## 7    compromiso_biopsicosocial     0
## 8  tenencia_de_la_vivienda_mod     0
## 9            num_otras_sus_mod     0
## 10     numero_de_hijos_mod_rec     0
## 11      motivodeegreso_mod_imp     0
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:   
#:#:#:#:#:#:#:#:#:#BASE DE DATOS DEFINITIVA#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
CONS_C1_df_dup_SEP_2020_women_miss_after_imp %>% 
  dplyr::filter(sum_miss==0) %>% 
  dplyr::select(-sum_miss) %>% 
#DAR FORMATO ORDINAL A LAS VARIABLES
  dplyr::mutate(edad_al_ing_grupos=parse_factor(as.character(edad_al_ing_grupos),levels=c('<18-29', '30-39', '40-49', '50+'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
    dplyr::mutate(escolaridad_rec=parse_factor(as.character(escolaridad_rec),levels=c('3-Completed primary school or less', '2-Completed high school or less', '1-More than high school'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "Latin1"))) %>%  
  dplyr::mutate(freq_cons_sus_prin=parse_factor(as.character(freq_cons_sus_prin),levels=c('Less than 1 day a week','2 to 3 days a week','4 to 6 days a week','1 day a week or more','Daily'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
  dplyr::mutate(compromiso_biopsicosocial=parse_factor(as.character(compromiso_biopsicosocial),levels=c('1-Mild', '2-Moderate','3-Severe'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
  dplyr::mutate(num_otras_sus_mod=parse_factor(as.character(num_otras_sus_mod),levels=c('No additional substance', 'One additional substance','More than one additional substance'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
  dplyr::mutate(fech_ing_next_treat_date=as.Date(fech_ing_next_treat)) %>% 
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
  data.table::data.table() %>% 
    assign("CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados",.,envir=.GlobalEnv)

#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_

attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$tipo_de_programa_2,"label") <- 'Type of program'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$edad_al_ing_grupos,"label") <- 'Age at admission to treatment, grouped.'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$estado_conyugal_2,"label") <- 'Marital status'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$numero_de_hijos_mod_rec,"label") <- 'Have children (Dichotomized)'

attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$sus_principal_mod,"label") <- 'Primary or main substance'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$num_otras_sus_mod,"label") <- 'Co-occurring SUD'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$freq_cons_sus_prin,"label") <- 'Consumption frequency of primary or main substance'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$compromiso_biopsicosocial,"label") <- 'Biopsychosocial involvement'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$escolaridad_rec,"label") <- 'Educational Attainment'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$motivodeegreso_mod_imp,"label") <- 'Cause of Discharge'
attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$fech_ing_next_treat_date,"label") <- 'Date of Admission to Posterior Treatment'


#attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$tipo_de_plan_res,"label") <- 'Setting of Treatment'
#attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$tipo_centro,"label") <- 'Type of center of the last entry'
#attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$embarazo,"label") <- 'Pregnant at admission'
#attr(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados$edad_ini_sus_prin_grupos,"label") <- 'Age at first use of principal substance, grouped'


We ended the process having 21,423 compelte cases (users= 21,423).


kableone <- function(x, ...) {
  capture.output(x <- print(x,...))
  knitr::kable(x,format= "html", format.args= list(decimal.mark= ".", big.mark= ","))
}
Variables_after_imp<-c("estado_conyugal_2","edad_al_ing_grupos","escolaridad_rec","sus_principal_mod","freq_cons_sus_prin","compromiso_biopsicosocial","tenencia_de_la_vivienda_mod","num_otras_sus_mod","numero_de_hijos_mod_rec","motivodeegreso_mod_imp","dias_treat_imp_sin_na")
catVars_after_imp<-
c("estado_conyugal_2","edad_al_ing_grupos","escolaridad_rec","sus_principal_mod","freq_cons_sus_prin","compromiso_biopsicosocial","tenencia_de_la_vivienda_mod","num_otras_sus_mod","numero_de_hijos_mod_rec","motivodeegreso_mod_imp")

pre_tab1<-Sys.time()
tab1<-
CreateTableOne(vars = Variables_after_imp, 
               strata = "tipo_de_programa_2", 
               data = CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados, 
               factorVars = catVars_after_imp, 
               smd=T)
post_tab1<-Sys.time()
diff_time_tab1=post_tab1-pre_tab1

kableone(tab1, 
         caption = paste0("Table 4. Covariate Balance in the Variables of Interest"),
         col.names= c("Variables","Ambulatory","Residential", "p-values","test","SMD"),
         nonnormal= c("edad_ini_cons","edad_al_ing","fech_ing_num"),#"\\hline",
                       smd=T, test=T, varLabels=T,noSpaces=T, printToggle=T, dropEqual=F) %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover","condensed"),font_size= 11) %>%
  #()
  row_spec(1, bold = T, italic =T,color ="black",hline_after=T,extra_latex_after="\\arrayrulecolor{white}",font_size= 11) %>%
  scroll_box(width = "100%", height = "400px") 
#"tipo_de_plan_ambulatorio",
#https://cran.r-project.org/web/packages/tableone/vignettes/smd.html
#http://rstudio-pubs-static.s3.amazonaws.com/405765_2ce448f9bde24148a5f94c535a34b70e.html
#https://cran.r-project.org/web/packages/tableone/vignettes/introduction.html
#https://cran.r-project.org/web/packages/tableone/tableone.pdf
#https://www.rdocumentation.org/packages/tableone/versions/0.12.0/topics/CreateTableOne

## Construct a table 
#standardized mean differences of greater than 0.1

Multi-state


#  dplyr::filter(motivodeegreso_mod_imp!="En curso")%>% #Sacar los tratamientos que estén en curso 


tab1_lab<- paste0('Original C1 Dataset \n(n = ', formatC(nrow(CONS_C1), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1%>% dplyr::distinct(HASH_KEY)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab2_lab<- paste0('C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab1_5_lab<- paste0('&#8226; Duplicated entries\\l &#8226; Overlapping treatments of users\\l &#8226; Intermediate events of treatment (continuous referrals)')
tab22_lab<- paste0('C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020_women), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_women%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab4_lab<- paste0('Imputed C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020_women_miss_after_imp), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_women_miss_after_imp%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab3_5_lab<- paste0('C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_descartados), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_descartados%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab_miss <-paste0("  Impute missing values: (n=",CONS_C1_df_dup_SEP_2020_women_miss[,..vector_variables_only_for_imputation] %>% complete.cases() %>% janitor::tabyl() %>% dplyr::filter(.=="FALSE") %>% dplyr::select(n) %>% as.numeric() %>% format(big.mark=","),")")

#https://stackoverflow.com/questions/46750364/diagrammer-and-graphviz
#https://mikeyharper.uk/flowcharts-in-r-using-diagrammer/
#http://blog.nguyenvq.com/blog/2012/05/29/better-decision-tree-graphics-for-rpart-via-party-and-partykit/
#http://blog.nguyenvq.com/blog/2014/01/17/skeleton-to-create-fast-automatic-tree-diagrams-using-r-and-graphviz/
#https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html
#https://stackoverflow.com/questions/39133058/how-to-use-graphviz-graphs-in-diagrammer-for-r
#https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789802566/1/ch01lvl1sec21/creating-diagrams-via-the-diagrammer-package
#https://justlegal.be/2019/05/using-flowcharts-to-display-legal-procedures/
# paste0("No. of treatments: ",table(table(t_id_1)) %>% formatC(big.mark = ","),"; No. of controls: ",table(table(c_id_1))%>% formatC(big.mark = ","))
#
library(DiagrammeR) #⋉
grViz("digraph flowchart {
      # node definitions with substituted label text
      node [fontname = Times, shape = rectangle,fontsize = 9]        
      tab1 [label = '@@1']
      tab2 [label = '@@2']
      tab32 [label = '&#8226;Select women\\l&#8226;Select first treatments\\l',fontsize = 7]
      tab22 [label = '@@6']
      tab3 [label = '&#8226;Duplicated entries\\l&#8226;Intermediate events of treatment (continuous referrals)\\l',fontsize = 7]
      tab4 [label = '@@4']
      blank [label = '', width = 0.0001, height = 0.0001]
      blank1 [label = '', width = 0.0001, height = 0.0001]
      blank2 [label = '', width = 0.0001, height = 0.0001]
      tab5 [label = '&#8226;Logically Inconsistent candidates for imputation\\l&#8226;Ties in candidates for imputation\\l',fontsize = 7]

      # edge definitions with the node IDs
      tab1 -> blank [arrowhead = none,label='  Data wrangling and normalization process',fontsize = 8];
      blank -> tab3
      blank -> tab2
      tab2 -> blank1 [arrowhead = none,label='  Sample selection',fontsize = 8];
      blank1 -> tab32
      blank1 -> tab22
      tab22 -> blank2 [arrowhead = none, label= '@@7', fontsize= 8];
      blank2 -> tab5 
      blank2 -> tab4 [label='  Result of the imputation of missing values',fontsize = 8];
            subgraph {
              rank = same; tab3; blank;
            }
              subgraph {
              rank = same; tab32; blank1;
            }
            subgraph {
              rank = same; tab5; blank2;
            }
      }

      [1]:  tab1_lab
      [2]:  tab2_lab
      [3]:  tab1_5_lab
      [4]:  tab4_lab
      [5]:  ''
      [6]:  tab22_lab
      [7]:  tab_miss
      ")
#      {rank=same; 'tab2'' -> tab3 [label='',fontsize = 11]}; #⋉
#CONS_C1_df_dup_SEP_2020_irrs_health

Transition matrix

#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
## 3 ESTADOS SIMPLES ##
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
links<-data.frame(stringsAsFactors=FALSE,
  from = c(rep("Admitted to\nTreatment", 2),"Therapeutic\nDischarge"),
  to = c(rep(c("Therapeutic\nDischarge"),1), rep("Readmission", 2)))

links2<-data.frame(stringsAsFactors=FALSE,
   from = c(rep("Admitted to\nTreatment", 2),"Therapeutic\nDischarge","Discharge w/o\nClinical Advice"),
   to = c(rep(c("Therapeutic\nDischarge","Discharge w/o\nClinical Advice"),1), rep("Readmission", 2)))

set.seed(1400)
co <- layout.fruchterman.reingold(graph_from_data_frame(links, directed=TRUE))
#https://www.r-graph-gallery.com/248-igraph-plotting-parameters.html
#https://rstudio-pubs-static.s3.amazonaws.com/341807_7b9d1a4e787146d492115f90ad75cd2d.html
par(mfrow=c(1, 2))  
par(oma=c(2,2,2,2))  
  plot(graph_from_data_frame(links, directed=TRUE),asp = 0,
       layout= co,
     #vertex.label= rev(),
     vertex.color="white",
     vertex.size=50,
      vertex.size2=25,
     vertex.label.cex=1, 
     edge.arrow.size=1,
     edge.color="black",
     vertex.shape="rectangle",
     vertex.label.color="black",
      edge.curved=0,
      edge.width=1.5,
     vertex.label.dist=0,
     vertex.cex = 3)
title("a) Three-states Model (Simplest)", sub = "No recurring states; Absorving state: Readmission;\nOther causes of discharge are not events of interest;\nModdified version of an illness-death model") ## internal titles

set.seed(100)
co2 <- layout.fruchterman.reingold(graph_from_data_frame(links2, directed=TRUE))

  plot(graph_from_data_frame(links2, directed=TRUE),asp = 0,
       layout= co2,
     #vertex.label= rev(),
     vertex.color="white",
     vertex.size=50,
      vertex.size2=25,
     vertex.label.cex=1, 
     edge.arrow.size=1,
     edge.color="black",
     vertex.shape="rectangle",
     vertex.label.color="black",
      edge.curved=0,
      edge.width=1.5,
     vertex.label.dist=0,
     vertex.cex = 3)
title("b) Four-states Model", sub = "No recurring states; Absorving state: Readmission;\n For now, includes Referrals as a discharge /wo Clinical Advice") ## internal titles

#dev.off()
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
## Probando con paquetes estadísticos
if(no_mostrar==1){
library(igraph)
Nodes <- c("Admitted to\nGP","Admitted to\nWE","Therapeutic\nDischarge","Discharge w/o\nClinical Advice","Readmission") #states possible in MSM
Edges <- list("Admitted to\nGP"=list(edges=c("Therapeutic\nDischarge","Discharge w/o\nClinical Advice")),
              "Admitted to\nWE"=list(edges=c("Therapeutic\nDischarge","Discharge w/o\nClinical Advice")),
              "Therapeutic\nDischarge"=list(edges=c("Readmission")),
              "Discharge /wo\nClinical Advice"=list(edges=c("Readmission")),
              "Readmission"=list(edges=NULL)) #transitions from each state

RCLTtree <- new("graphNEL",nodes=Nodes,edgeL=Edges,edgemode="directed")
plot(RCLTtree)
library(igraph)

#https://www.rdocumentation.org/packages/msSurv/versions/1.2-2/topics/msSurv
msSurv(LTRCdata, RCLTtree, cens.type="ind", LT=FALSE, bs=FALSE, B=200)
}
#Data should be in a data frame with column names "id", "stop", "st.stage", and "stage" where "id" is the individual's identification number, "stop" is the transition time from state j to j', "st.stage" is the state the individual is transitioning from (i.e., j), and "stage" is the state the individual is transitioning to (i.e., j') and equals 0 if right censored.


## 80% of sample is LT, rest has start time of 0
### AGS: Parten en 0, salvo que estén truncados a la izquierda. 
### Parece que todos comparten un mismo tiempo ojetivo.
### AGS: Cuando hay un estado seguido no es necesario interval censoring, se dn en tun tiempo continuo
### El 0 es censura

Session Info

Sys.getenv("R_LIBS_USER")
## [1] "C:/Users/CISS Fondecyt/OneDrive/Documentos/R/win-library/4.0"
rstudioapi::getSourceEditorContext()
## Document Context: 
## - id:        '25ECB0F5'
## - path:      'G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/SUD_CL/Proyecto_carla2.Rmd'
## - contents:  <2239 rows>
## Document Selection:
## - [2164, 137] -- [2164, 137]: ''
save.image("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/mult_state_carla.RData")

CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados%>%
  dplyr::arrange(hash_key, desc(fech_ing))%>% 
  rio::export(file = "G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/mult_state_carla.dta")

sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] DiagrammeR_1.0.6.1.9000 Amelia_1.7.6            Rcpp_1.0.5             
##  [4] compareGroups_4.4.5     gurobi_9.1-0            radiant.update_1.4.1   
##  [7] igraph_1.2.6            eha_2.8.1               cobalt_4.2.3           
## [10] sensitivityfull_1.5.6   sensitivity2x2xk_1.01   MatchIt_3.0.2          
## [13] tableone_0.12.0         stargazer_5.2.2         reshape2_1.4.4         
## [16] exactRankTests_0.8-31   gridExtra_2.3           foreign_0.8-80         
## [19] glpkAPI_1.3.2           designmatch_0.3.1       Rglpk_0.6-4            
## [22] slam_0.1-47             MASS_7.3-51.6           survMisc_0.5.5         
## [25] ggfortify_0.4.10        rateratio.test_1.0-2    survminer_0.4.8        
## [28] ggpubr_0.4.0            epiR_1.0-15             forcats_0.5.0          
## [31] purrr_0.3.4             readr_1.3.1             tibble_3.0.3           
## [34] tidyverse_1.3.0         treemapify_2.5.3        ggiraph_0.7.0          
## [37] chilemapas_0.2          sf_0.9-3                finalfit_1.0.1         
## [40] lsmeans_2.30-0          emmeans_1.4.8           choroplethrAdmin1_1.1.1
## [43] choroplethrMaps_1.0.1   choroplethr_3.6.3       acs_2.1.4              
## [46] XML_3.99-0.3            RColorBrewer_1.1-2      panelr_0.7.3           
## [49] lme4_1.1-23             Matrix_1.2-18           dplyr_1.0.1            
## [52] data.table_1.13.0       codebook_0.9.2          devtools_2.3.0         
## [55] usethis_1.6.1           sqldf_0.4-11            RSQLite_2.2.0          
## [58] gsubfn_0.7              proto_1.0.0             broom_0.7.0            
## [61] zoo_1.8-8               altair_4.0.1            rbokeh_0.5.1           
## [64] janitor_2.0.1           plotly_4.9.2.1          kableExtra_1.1.0       
## [67] Hmisc_4.4-0             Formula_1.2-3           survival_3.1-12        
## [70] lattice_0.20-41         ggplot2_3.3.2           stringr_1.4.0          
## [73] stringi_1.4.6           tidyr_1.1.1             knitr_1.29             
## [76] matrixStats_0.56.0      boot_1.3-25            
## 
## loaded via a namespace (and not attached):
##   [1] class_7.3-17        ps_1.3.3            rprojroot_1.3-2    
##   [4] crayon_1.3.4        V8_3.1.0            nlme_3.1-148       
##   [7] backports_1.1.7     reprex_0.3.0        rlang_0.4.7        
##  [10] readxl_1.3.1        performance_0.4.8   nloptr_1.2.2.2     
##  [13] callr_3.4.3         flextable_0.5.10    rjson_0.2.20       
##  [16] ggmap_3.0.0         bit64_0.9-7         glue_1.4.1         
##  [19] sjPlot_2.8.4        parallel_4.0.2      processx_3.4.3     
##  [22] classInt_0.4-3      tcltk_4.0.2         haven_2.3.1        
##  [25] tidyselect_1.1.0    km.ci_0.5-2         rio_0.5.16         
##  [28] sjmisc_2.8.5        chron_2.3-55        xtable_1.8-4       
##  [31] magrittr_1.5        evaluate_0.14       gdtools_0.2.2      
##  [34] RgoogleMaps_1.4.5.3 cli_2.0.2           rstudioapi_0.11    
##  [37] sp_1.4-2            rpart_4.1-15        jtools_2.0.5       
##  [40] sjlabelled_1.1.6    RJSONIO_1.3-1.4     maps_3.3.0         
##  [43] gistr_0.5.0         xfun_0.16           parameters_0.8.2   
##  [46] pkgbuild_1.1.0      cluster_2.1.0       ggfittext_0.9.0    
##  [49] png_0.1-7           withr_2.2.0         bitops_1.0-6       
##  [52] plyr_1.8.6          cellranger_1.1.0    e1071_1.7-3        
##  [55] survey_4.0          coda_0.19-3         pillar_1.4.6       
##  [58] multcomp_1.4-13     fs_1.5.0            vctrs_0.3.2        
##  [61] ellipsis_0.3.1      generics_0.0.2      rgdal_1.5-8        
##  [64] tools_4.0.2         munsell_0.5.0       compiler_4.0.2     
##  [67] pkgload_1.1.0       abind_1.4-5         tigris_0.9.4       
##  [70] sessioninfo_1.1.1   visNetwork_2.0.9    jsonlite_1.7.0     
##  [73] WDI_2.6.0           scales_1.1.1        carData_3.0-4      
##  [76] estimability_1.3    lazyeval_0.2.2      car_3.0-8          
##  [79] latticeExtra_0.6-29 reticulate_1.16     effectsize_0.3.2   
##  [82] checkmate_2.0.0     rmarkdown_2.6       openxlsx_4.1.5     
##  [85] sandwich_2.5-1      statmod_1.4.34      webshot_0.5.2      
##  [88] pander_0.6.3        yaml_2.2.1          systemfonts_0.2.3  
##  [91] htmltools_0.5.0     memoise_1.1.0       viridisLite_0.3.0  
##  [94] jsonvalidate_1.1.0  digest_0.6.25       assertthat_0.2.1   
##  [97] rappdirs_0.3.1      repr_1.1.0          bayestestR_0.7.2   
## [100] BiasedUrn_1.07      KMsurv_0.1-5        units_0.6-6        
## [103] remotes_2.2.0       blob_1.2.1          splines_4.0.2      
## [106] labeling_0.3        hms_0.5.3           rmapshaper_0.4.4   
## [109] modelr_0.1.8        colorspace_1.4-1    base64enc_0.1-3    
## [112] nnet_7.3-14         mvtnorm_1.1-1       fansi_0.4.1        
## [115] truncnorm_1.0-8     R6_2.4.1            grid_4.0.2         
## [118] crul_0.9.0          lifecycle_0.2.0     acepack_1.4.1      
## [121] labelled_2.5.0      zip_2.1.1           writexl_1.3        
## [124] curl_4.3            geojsonlint_0.4.0   ggsignif_0.6.0     
## [127] pryr_0.1.4          minqa_1.2.4         testthat_2.3.2     
## [130] snakecase_0.11.0    desc_1.2.0          TH.data_1.0-10     
## [133] htmlwidgets_1.5.1   officer_0.3.13      crosstalk_1.1.0.1  
## [136] rvest_0.3.6         insight_0.9.0       htmlTable_2.0.1    
## [139] codetools_0.2-16    lubridate_1.7.9     prettyunits_1.1.1  
## [142] dbplyr_1.4.4        vegawidget_0.3.1    gtable_0.3.0       
## [145] DBI_1.1.0           httr_1.4.2          highr_0.8          
## [148] KernSmooth_2.23-17  uuid_0.1-4          hexbin_1.28.1      
## [151] mice_3.11.0         xml2_1.3.2          ggeffects_0.15.1   
## [154] bit_1.1-15.2        sjstats_0.18.0      jpeg_0.1-8.1       
## [157] pkgconfig_2.0.3     maptools_1.0-1      rstatix_0.6.0      
## [160] mitools_2.4         HardyWeinberg_1.6.6 Rsolnp_1.16        
## [163] httpcode_0.3.0